Parker 2006: An Urban Myth?

If you are not a climate scientist (or a realclimate reader), you would almost certainly believe, from your own experience, that cities are warmer than the surrounding countryside – the “urban heat island”. From that, it’s easy to conclude that as cities become bigger and as towns become cities and villages become towns, that there is a widespread impact on urban records from changes in landscape, which have to be considered before you can back out what portion is due to increased GHG.

One of the main IPCC creeds is that the urban heat island effect has a negligible impact on large-scale averages such as CRU or GISS. The obvious way of proving this would seem to be taking measurements on an urban transect and showing that there is no urban heat island. Of course, Jones and his associates can’t do that because such transects always show a substantial urban heat island. So they have to resort to indirect methods to provide evidence of “things unseen”, such as Jones et al 1990, which we’ve discussed in the past.

The newest entry in the theological literature is Parker (2004, 2006), who, once again, does not show the absence of an urban heat island by direct measurements, but purports to show the absence of an effect on large-scale averages by showing that the temperature trends on calm days is comparable to that on windy days. My first reaction to this, and I’m sure that others had the same reaction was: well, so what? Why would anyone interpret that as evidence one way or the other on UHI?

So let’s backtrack through Parker’s logic, such as it is, and see why he believes that calm-windy is relevant to the existence of a UHI effect.

Parker 2006 summarized his results as follows:

The main impact of any urban warming is expected to be on Tmin on calm nights (Johnson et al. 1991). However, for 1950-2000, the trends of global annual average Tmin for windy, calm, and all conditions were virtually identical at 0.20°C – 0.06°C decade (Fig. 4a,b and Table 1).

Now, from my own direct experience, whenever I see someone from the Team paraphrase an article and say that their “main” conclusion is X, I do not assume that the original article said that at all. (In criticizing our articles for example, they never quoted directly from anything we said, but always paraphrased and then attacked a straw man). So what did Johnson et al 1991 actually say? Their abstract stated that the urban heat island was “most strongly” developed in calm nights, a point re-stated in their opening paragraphs also whown below.

Other than this, Johnson et al 1991 is silent on the topic. Oke et al 1991 is a companion study that is much more informative about UHI, with interesting discussions of the contributions to UHI of canyon view, thermal storage, anthropogenic heat emissions, an urban greenhouse effect from additional pollution and moisture, surface emissivity etc. They develop a model for calm nights because it is more tractable, but comment on the effect of wind on UHI as follows:

All this says is that wind attenuates the UHI somewhat. If all other factors (the economist’s ceterius paribus) stayed the same, then one would expect the slope of the trend on windy nights to be a titch lower than the slope on calm nights, depending on the amount of attenuation. But any change in the ceteris paribus could easily change things. Oke mentions that driving in the evening increases turbulence. So if the amount of evening driving increased in the evenings in cities over the past 50 years, then this would obviously impact the comparison.

Other points in passing. Parker 2006 says (without attribution):

Any urban warming signal should be most evident in summer, when urban heat islands are stronger owing to greater storage of solar heat in urban structures.

No such statement is made in Oke et al 1991 or Johnson et al 1991, where nuanced discussions are made, with Fairbanks and Moscow being mentioned as examples of very large winter UHI effects.

Parker says of Shanghai:

Shanghai, although not sharing a 5° grid box, was not used as it showed marked cooling on calm relative to windy days and nights, possibly indicating a site change.

If Shanghai doesn’t fit Parker’s urbanization metric, one feels that Parker may well be measuring something that is not a proxy

He observes of Fresno, California (See his Figure B1) and this may be of interest for station inspection:

A discontinuity at Fresno, California, is illustrated in Fig. B1. Because changes of siting, instrumentation, or observing practice are likely to have the greatest impact in calm, cloudless weather (see main text), analyses like Fig. B1 could be used, along with appropriate statistical tests, to supplement or validate station metadata.

But the real question in all of this is why would one use a “proxy” like trends between windy-calm nights (and there’s lots of hair on the wind-calm information which comes from NCEP re-analysis on a gridcell basis only) to analyze UHI when temperatures can be measured directly across a transect. Here’s a figure from Oke et al showing UHI measurements in a variety of urban settings.

488 Comments

Why would they suggest that having no difference in trend between calm and windy nights is evidence of a lack of UHI? All that proves is that the difference in UHI between windy and calm conditions is smaller than the difference between a UHI affected station and a rural station. Measuring the difference in trend between calm and windy days simply measures the magnitude of the wind’s impact to the total UHI affect.

“One of the objectives in establishing the U.S. HCN was to detect secular changes of regional rather than local climate. Therefore, only those stations that were not believed to be influenced to any substantial degree by artificial changes of local environments were included in the network. Some of the stations in the U.S. HCN are first order weather stations, but the majority were selected from U.S. Cooperative Weather Stations. To be included in the U.S. HCN, a station had to be active and have at least 80 years of mean monthly temperature and total monthly precipitation data, and have experienced few station changes.”

The IPCC has sensitive radar for anything published that supports their attempts to show a need for mitigating AGW. If it does it gets referenced without any or little critical review and comment. The IPCC referencing in turn lends a certain amount of credence to the published article and it gets quoted as a main authority on the matter, and in effect creates a circular enhancement process.

On the other hand, articles that may supply counter evidence to their favored articles are either ignored or given short shrift or given the low confidence signal with a “speculative” or “needs more investigation” label. I think in those terms the Mann HS and the Parker negligible UHI effect are similar.

I was always amazed that Parker’s very indirect approach to measuring UHI was able to be maintained as authoritative.

The main impact of any urban warming is expected to be on Tmin on calm nights (Johnson et al. 1991).

Not in my experience. This seems like a contorted roundabout way to prove something. I live about 15 miles from Dallas and I have often watched the temperature rise by a few degrees in my car’s thermometer as I drive into the city. I have also watched the temperature drop as I drive out of the city. The rise begins well into the outskirts of the city and probably continues beyond where I live. I have noticed this effect both in the daytime and at night. Maybe I’ll collect some data. I have also noticed this effect on the local weather reports. Small nearby towns often have lower temperatures, regardless of whether they are north, south, east, or west of the main city. This shouldn’t be too difficult to quantify. Didn’t Hansen publish a study in which he used satellite data to somehow determine the urban hot spots and remove the UHI signal?

Abstract
Investigations concentrated on the temporal dynamics of the urban heat island (UHI) during the night in Szeged,
Hungary. Task includes the revelation of building and re-building of the UHI along an urban cross-section studying
some cases in the warmer and colder seasons and the explanation of their features using land-use and
meteorological parameters.
The UHI formations were rather perfect with the highest values in the city centre and a few hours after sunset.
However, some assimetry occurs in the isotherms because they are shifted a bit to the eastern edge of the
transect. It can be attributed to the influence of the highest built-up density of this neighbourhood. Using
normalized UHI values some interesting features in the profiles emerge. Presumably, the changes in the
magnitudes of UHI in the western and eastern suburbs are caused by the cooler rural air transport (first from NW
then from E-NE) according to the changed wind direction.
Key-words: UHI, urban cross-section, summer and winter cases, Szeged, Hungary

Hehe, I’m not sure you could ever get an accurate reading of windy vs. calm here in CO. Maybe you could get a “40 mph windy” vs. “20 mph windy” comparison. In the summer, once it gets up into the 90 degree range, the wind does nothing to cool us off: it turns the area into a convection oven. I bet I drink 16 glasses of water a day in July, else I suffer from perpetually chapped lips. 🙂

We encounter an obvious UHI effect every morning while listening to the weather on the local radio. They always quote temperatures from Edmonton International Airport (YEG) about 10km from the south edge of the city, and Edmonton Municipal Airport (YXD) in the geographic centre of the city. Anecdotally the difference is 4c year-round, with up to 8c during one of our wonderful -40c cold snaps.

I’ve not seen a representation of how Canadian data has been incorporated into the climate models. Does anyone have a link?

I don’t think it is clear from the article that the Parker paper explicitly states that the UHI effect does exist.

Essentially it is saying that comparing trends of calm and windy nights suggests that the increase in the UHI effect is not significant over 50 years.

Analysis of transects of a range of cities in a range of conditions will help to qualify the data, but won’t show anything about the historical changes in urban-ness that would influence the trend in temperatures.

Thanks for making available David E. Parker’s “A Demonstration that Large-Scale Warming is Not Urban” (2006).

I would like to present a more formal rendition of Parker’s argument. I think it might help to clarify some points in dispute, such as:
– to what extent windiness affects the degree of UHI
– local site issues
– the simultaneity of wind and temperature measurements

I define two indices:

– j: The number of the weather-station site. j runs from 1 to N.
– n: The number of the day. Since the dates of study run from 1950 to 2000, n runs from 1 to about 18,263.

Let A(j,n) be the minimum temperature measured at site j on day n.

Let B(j,n) be the maximum temperature measured at site j on day n.

It can be presumed that A(j,n) will be a night-time measurement, and can thus be affected by near-surface temperature inversions (when the ground cools by IR radiation, but the air above does not cool as rapidly). B(j,n) will be a day-time measurement, and will usually not be affected by a near-surface temperature inversion, except during wery calm weather conditions in the winter, in which case there can be a persistent near-surface temperature inversion.

We assume that the measurements at site j will be affected by an urban-heat island effect UHI(j, n). This means that

Eqn.(1):
A(j,n) = Tmax(j,n) + UHI(j,n)

where Tmax(j,n) is the maximum temperature you would have measured at site j and day n, had all the urbanization, asphalt, burning cans, etc. not been present; and UHI(j,n) represents the impact of all that stuff.

Likewise,
Eqn.(2):
B(j,n) = Tmin(j,n) + UHI(j,n) ‘€” NSTI(j,n)

where Tmin(j,n) is the minimum temperature you would have measured at site j and day n, without all the stuff; UHI(j,n) is the stuff again; and NSTI(j,n) is the near-surface temperature inversion. Since NSTI is cooling at the level appropriate to weather stations (near the ground), it is generally a positive quantity.

A(j,n) and B(j,n) are simple time series. However, following Parker, I am going to do an experiment in data analysis. I am going to divide the days into two classes: windy days (set W), and calm days (set C). (These apply to one site only, of course.) Using the language of sets: {1, 2, … N} = W U C ; or if you don’t like my use of the Roman letter U for the set-union symbol, {1, 2, … N} = W + C.

On calm days, I expect both the UHI effect and the NSTI to be operating in full force: whatever heating is provided by the urbanization will be operating without being blown away; and whatever cooling is provided by the NSTI will also be protected by the stratification of the boundary layer.

However, on windy days, I expect the UHI effect to be vitiated by mixing of air from outside the region of the city with the relatively warmed air; and I expect the windiness to reduce the stratification of the boundary layer (“mix it up”) and thus reduce the cooling effect of the NSTI.

Therefore, whereas on calm days, I still get the original equations, on windy days, the impact of the UHI and the NSTI are both reduced. For simplicity, let’s assume that they are reduced by the same factor r. (This isn’t true; but it will simplify the discussion; and you can go back later and verify that giving them each their own reduction factor won’t change anything.). In that case, what we should really say is that;

where the *’d functions are the slowly-varying seasonal values. In other words, UHI* is the value of the urban-heat-island effect if wind were not reducing it by replacing warmer air with colder; and NSTI* is the effect of the near-surface temperature inversion if the wind were not mixing up the air near the ground with the air a little higher up. Both these starred values should be more slowly varying, because the jitteriness of the windiness factor has been taken out. They should vary on more of a seasonal basis, rather than on a day-to-day timescale.

Each of these two sets of equations applies to different sets of days. In order to compare the behavior of these two sets of functions, I am going to define two sets of extended functions:

Eqn.(7):
Ac(j,n) = A(j,n) when j is in set C
Ac(j,n) = the interpolation of A(j,n) when j is in set W

Bc(j,n) = B(j,n) when j is in set C
Bc(j,n) = the interpolation of B(j,n) when j is in set W

Aw(j,n) = A(j,n) when j is in set W
Aw(j,n) = the interpolation of A(j,n) when j is in set C

Bw(j,n) = B(j,n) when j is in set W
Bw(j,n) = the interpolation of B(j,n) when j is in set C

In other Aw and Bw are the maximum and minimum temperatures on windy days, and are the interpolations of these values on calm days; and Ac and Bc are the maximum and minimum temperatures on calm days, and are the interpolations of these values on windy days. All 4 functions are defined on all N days.

In terms of Parker’s graphs, the ‘€”c functions correspond to what Parker called “calm day” plots, and the ‘€”w functions correspond to what Parker called “windy day” plots: the dotted and dashed curves, respectively.

The point is this: If you take the trend of the A curves from 1950 to 2000, you should get:
Eqn.(9):
Trend [Ac(j,n)] = Trend [Tmax(j,n)] + Trend [UHI*(j,n)]
Trend [Aw(j,n)] = Trend [Tmax(j,n)] + r Trend [UHI*(j,n)]

a) Under this analysis, we see that the difference in calm-day trends and windy-day trends reflects directly whatever is happening with the UHI trend. If there is no difference in these two trends, either (1-r) = 0, or else there is no UHI* trend. (Eqn(12)).
b) But if (1-r) = 0, Ac = Aw and Bc = Bw (Eqn.(8)). That mean that the calm-day interpolated temperatures equalled the windy-day interpolated temperatures. And that is just not true: All of Parker’s graphs show a gap between them. (This is what I meant by saying that “the wind blows the UHI away”: r is less than 1.)
c) a) and b) together imply that there is no trend in the UHI*, if there is no difference in the windy-day and calm-day trends.

Since, in the majority of cases, Parker’s analysis showed there was no difference in the calm-day and windy-day trends, there can be no significant UHI* trend in these cases.

As noted in his papers, there were a few cases when he did detect a UHI* trend.

I have divided the days up into windy and calm. You could also subdivide them into 3 or more sets (as Parker did), as long as you have enough members in each set to do sensible interpolation, and thus get reasonable curves.

Appendix A details Parker’s efforts to make sure that the wind measurements and the temperature measurements were as properly tied together as possible, taking into account different time-stamping procedures in different countries. Given that it doesn’t seem to be possible to get absolutely simultaneous measurements over such an extended period in the past (1950-2000), what will this inaccuracy do? It will cause a degree of misallocation: Some days that were windy will be called calm, and vice versa, so the Ac & Aw and the Bc & Bw curves will be brought closer together, reducing the estimate of (1-r) UHI*. So Parker’s gap should represent a lower estimate for the amount of (1-r) UHI*. It’s reasonable to expect that the estimate is not too bad: there has to be some reasonable degree of auto-correlation in time for wind measurements, and the Parker gap hasn’t disappeared.

“The robustness of the analysis to the criterion for “calm” implies that the estimated
overall trends are insensitive to boundary layer structure and small-scale advection, and to siting, instrumentation, and observing practices that increasingly influence temperatures as winds become lighter.” and “In view of the error estimates (Tables 1 and 2) and
the different periods of analysis, the results are compatible with the IPCC TAR conclusion that urban warming is responsible for an uncertainty of 0.06°C in the global warming in the twentieth century.”

I have one question: Did Mr. Parket actually visit any of the weather stations to see what influences (UHI or otherwise) may be affecting the wind and temperature gathering and hence the data, or is this another armchair data wrangling study?

Given what I’ve seen in the past month in weather station surveys, and looking at the surveys done by others such as Pielke, Taylor, and Niyogi et al, and given the fact that the temperature data used in the study appears to be highly proxyed and filtered, I have no choice but to call conclusions on this paper dubious at best.

I an regularly “gobsmacked” at the poor quality of the surface data collection instrumentation and the microsite bias examples that seem to be coming in daily, yet researchers such a Parker treat the data as if it were faultless, and quote figures such as “uncertainty of 0.06°C” when the figure is below the noise level of the instrumentation.

As far as I can tell the only supportable conclusion that can be drawn from this paper is that windy days spread around a portion of the UHI effect via mass transfer.

It sounds suspect when you have to sell a simple concept like AGW with tree rings from bristle cone pines. Same goes for showing a simple concept like UHI has minimal effects by complicating it with analysis of temps on windy verses calm days.

A number of responsibly placed and monitored thermometers, around several responsibly selected urban and rural environments, set up for climate monitoring rather than weather reporting, and two years worth of responsible data collection and you would have a relatively perfect answer to the UHI question. Am I missing something?

But the Hadley Centre has to get this funded instead. It’s not like UHI is a brand new concept this year and they haven’t had time to get it together. If these folks are actually interested in getting the real answer they should perform simple tests with 2 years of good data rather than complicated tests with 50 years of questionable data.

#17. Here in Boston, the airport is a “cool park”. The local temperature is measured at Logan Airport, which is right on the Atlantic. Out here in the ‘burbs, 10 miles from the airport, the temperature is regularly 5 to 10 degrees warmer than the reported temperatures when the wind is blowing from the east. When the wind is from the west the temperatures tend to be equal.

Re #15
Mr. King, thanks for the formal statement, although I am not sure Parker would agree with it 100%. I understood your statement much better than I did Parker’s explication.

Since the formal statement and the Parker paper did not track cloudiness trends, I think they are both subject to criticism as cloudiness is admitted to be an important issue. Parker tried to capture some cloudiness data, but discarded it as imperfect and also not useful, as I read his paper. Certainly there must be data on cloudiness trends Parker could have quoted to say it didn’t matter or did matter.

I also take exception to the assumption that trends in near surface temperature inversion effects are trendless. Not that I have any data to the contrary of course, but on the theory that hotter air over time might cause the rural ground-air temperature difference to increase at T-min times and thus cause a greater inversion effect over time. I also think that increasing urbanization would affect this factor, as urban surfaces would get hotter than the air temperature during the day and would not be as likely to cool at night to a temperature below the air temperature because they started out hotter at sundown and they have more thermal mass.

Looking at Parker again, his article is even worse than I portrayed it initially. Oke talks about UHI as an urban-rural differential. Parker doesn’t ever mention urban-rural differentials, he talks about Tmin.

Now Tmin typically occurs around dawn. Parker says that the greatest UHI impact is on Tmin attributing this to Johnson et al 1991 – thus around dawn. Parker:

The main impact of any urban warming is expected to be on Tmin on calm nights (Johnson et al. 1991).

As Eli Rabett/Josh Halpern likes to say, RTFR. Jphnson et al say nothing of the sort. Nowhere do they connect UHI specifically to Tmin. Actually, they say something completely opposite. They say that the greatest UHI is just after sunset, when everything is still warm:

So Parker’s supposed authority for his “proxy” argument gives no support whatever for this particular urban myth. And I suspect that none of the stadiums of IPCC AR4 reviewers objected.

As a native of Fresno and graduate of Fresno State, I would encourage you to read Christy et al. 2006 (J. Climate) where the trends in Central California temperatures have been reconstructed in the most detailed manner. Bottom line: since 1910 – rapid warming in TMin in the Valley but no change in the adjacent Sierras, indicating land use change as the cause. Turbulent mixing of warm air downward likely more common in recent years due to roughness changes, urbanization, sensible heat flux from warm, dark, irrigated vegetation etc. See also Pielke Sr. et al. 2007 and on 22 Jun, Walters et al. 2007 GRL for theory of warming TMin.

Looking at the second number minus the third, in every case except the tropics, the windy trend is greater than the calm trend. The other exception is the global number, where the trends are equal. Seems to be the sum of the parts exceeds the whole in this regard.

It is also fun to note that Parker bemoans major gaps in the data for the tropics in his paper, yet assigns a two sigma error measurement of .04 degrees C in the trend data above for the .18 degree C trend for the tropics, which is less than the .05 degree C two sigma error measurement for the .20 degree trend shown for the whole globe. Some of the other 2 sigma error estimates suggest that the statistics package did most of the thinking on the error calculation and reporting.

The max temp table raises the same questions.

Lastly Parker does not seem to speculate on the fairly consistent higher trend of temperature increase he found on windy days compared to calm days, except to say it is the opposite of an urban warming signal and earlier in his paper to speculate that the windy days might not be as impacted by bad temperature sensing apparatus and siting. Where are the hot winds coming from? Jet engines? Relatively hot air from the runways and structures and streets nearby? Or is it just plain urban cooling, but he was too shy to say it?

Steve, first, thanks to Dr. Parker for his prompt provision of the data.

Second, I think that the difficulty in the study (in addition to the inversion issue above) is that the wind data has suffered at least four generalizations:

1) It is not data per se, but the result of the NCEP/NCAR reanalysis (read “computer model output”) results.

2) It is gridded, rather than specific to the station.

3) The stations can be a ways from the center of the gridcell. The average distance from the gridcell center is 90 km, with the maximum distance 180 km.

4) The winds are only given for four times during the day – 0, 6, 12, and 18 GMT. These may not correspond with the times of the local minima/maxima.

He does not say how he has dealt with problem number 4. Did he take the nearest value, or interpolate between the values, or what?

If he takes the nearest value, there’s another problem. Suppose the local minimum temperature occurs at say 0200 GMT. He uses the 0000 value. But as the year progresses, the minimum temperature occurs earlier and earlier in the day, until it is at 0400 Zulu. So at some point, he’s switching between the wind before the minimum to the wind after the minimum.

And interpolating the temperatures has its own problems. Winds are generally calmer during the night, but at dawn, there is a “dawn breeze” because of the temperature difference between the lighted and unlighted portions of the globe. Depending on the latitude, however, this “dawn breeze” can either oppose or intensify the average wind. In the tropics and north of about 60N, it opposes the wind on average, so the wind often drops at dawn. This, of course, affects the timing of the minimum temperature. In either case, the wind starts to build rapidly from that point, typically peaking between 12:00 and 2:00 (local time) and slowly dying after that. And of course, all of this changes with the seasons.

The most troublesome of these generalizations to me is the use of the gridded values. I live in a valley between two high mountains on the Big Island in Hawaii. When the trade winds blow, here in the valley they shriek … but 20 miles away, it can be just light breezes. Wind is not like temperature, where the anomalies can be related over a fairly large distance. It is very site-specific, which is why such trouble is taken with things like the siting of windmills.

Because of these problems, I see no reason to believe that a global average, using wind data that is this coarse and non site-specific, will reveal much of anything.

The village of Barrow (71°N latitude) is the largest native community in the Arctic, with a population of approximately 4500 people. Situated on the coast of the Arctic Ocean in northernmost Alaska, the area is entirely underlain by permafrost. Although most supplies must be imported, Barrow relies on local natural gas fields to meet all energy requirements for building heat and electrical power generation. This energy eventually dissipates into the atmosphere, and can be detected as a pronounced urban heat island (UHI) in winter. Since 2001, a 150 km2 area in and around Barrow has been monitored using ‘ˆ¼70 data loggers recording air temperature at hourly intervals. The mean daily temperature of the urban and rural areas is calculated using a representative sample of core sites, and the UHI magnitude (MUHI) is calculated as the difference in the group averages. The MUHI is most pronounced in winter months (December’€”March), with temperatures in the urban area averaging 2°C warmer than in the surrounding tundra and occasionally exceeding 6°C. The MUHI is maximized under cold and calm conditions, and decreases with wind speed and warmer temperatures. It is strongly and directly correlated to natural gas utilization on a monthly basis. Integrated over the home heating season, there is an 8% reduction in freezing degree days in the village. It is unlikely that anthropogenic heat contributes to the forward shift in the snow meltout date that has been observed near Barrow over the past 60 years.

Arnfield, A. J. 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology 23, 1-26.

Abstract
Progress in urban climatology over the two decades since the first publication of the International Journal of Climatology is reviewed. It is emphasized that urban climatology during this period has benefited from conceptual advances made in microclimatology and boundary-layer climatology in general. The role of scale, heterogeneity, dynamic source areas for turbulent fluxes and the complexity introduced by the roughness sublayer over the tall, rigid roughness elements of cities is described. The diversity of urban heat islands, depending on the medium sensed and the sensing technique, is explained. The review focuses on two areas within urban climatology. First, it assesses advances in the study of selected urban climatic processes relating to urban atmospheric turbulence (including surface roughness) and exchange processes for energy and water, at scales of consideration ranging from individual facets of the urban environment, through streets and city blocks to neighbourhoods. Second, it explores the literature on the urban temperature field. The state of knowledge about urban heat islands around 1980 is described and work since then is assessed in terms of similarities to and contrasts with that situation. Finally, the main advances are summarized and recommendations for urban climate work in the future are made.

Steve: Check e-mail for “Lighthouses as Unbiased Global Thermometers. This is a copy of my report that I sent Roy Spencer and John Christy who recommended yesterday that I send it to you. The title of the report is, Lighthouses: Thermometers for Accurate and Unbiased Measurement of Air Temperature at the Sea-Land Interface.

I live in BC and there are about 80 lighthouses, some with records that go back before 1900. Here are conditions: sample interval, March 16-26; only min temp used; and years that have approx the same El Nino and La Nina index were selected. Here are results for the Quatsino LIghthouse that I just finished about ten min. ago. El Nino years: 1900, 276.4 +/- 2.5 deg K, En index=1.8; 1998, 277.3 +/- 1.7 deg K EN index=3.3. La Nina Years: 1899, 273.3 +/- 1.0 deg K, LN index= -.05,
1999, 275.5 +/- 1.8 deg K LN index= -0.8. A they say, the data speak for themselves and they say the temperature at the spring equinox has remained unchanded for a century ie, no detectable global warming.

Note the difference between the means for the El Nino and La Nina years at 1900 and century later. Clearly this method will let us determine once and for allif any significant global warming has taken place. The answer is on the lighthouse walls and is NO!. End of story It’s game over. Al Gore I want a refund on the stupid video!

One of the main IPCC creeds is that the urban heat island effect has a negligible impact on large-scale averages such as CRU or GISS. The obvious way of proving this would seem to be taking measurements on an urban transect and showing that there is no urban heat island.

No! The study here is looking at temperature trends due to increasing urbanisation. More transects do not help. Parker explicitly states that he doesn’t dispute that UHI exists.

Steve has picked up on the fact that the Johnson citation says nothing about Tmin being affected by UHI. This means we have two possibilities:

1. Tmin is affected by UHI and Parker is right despite the fact that the evidence is not fully there.
2. Tmin is not affected by UHI – Parker is wrong. But then so is UHI. Therefore the warming trends are correct.

This equation implicitly assumes that Tmax is unaffected by changes in wind and is the same as it is for calm conditions. It is virtually certain that this is not the case. Windy conditions are associated with close isobars and lows, and thus will tend to have lower temperatures associated with them.

Consequently, you have one equation with two unknowns, r and r’. r and r’ are not likely to be the same. It is not possible to disentangle these effects and pull out a UHI signal using this technique.

The proper technique is to do trisects of UHIs. Around 1900 in many places, one can assume such a trisect would be a flat line (no UHI). Today the trisect would be a gaussian-like hill. The difference between where the USCHN station is on the hill and the zero line in 1900 will allow you to determine the trend per decade of UHI warming.

Let me propose a solution to Parker’s results, building on a number of observations. Also influenced by an exceptionally interesting article Godowitch et al,, 1985. Evolution of the Nocturnal Inversion Layer at an urban and nonurban location, J Clim Appl Met 791 ff here .

First, all (or nearly all) the U.S. sites in the Parker network are from airports. I haven’t parsed non-US locations yet, but it’s a fair surmise that nearly all the non-US Parker sites are airports as well.

Second, in many locations that I’ve travelled to over my life, airports have been a type of hub for suburbanization. In Toronto, the airport that was in remote suburbs when I was a boy is now surrounded by suburbs for miles around with the busiest stretch of highway in the country running by the runways. A couple of years ago, an airplane overshot the runway and almost ended up on the highway.)

Third, as Joe observed in #25, in nearly every case, the “windy” trend is slightly greater than the “calm” trend. Let us embrace this fact and try to explain it.

Fourth, several posters have tried to formulate the Parker results a little more formally. I’m going to follow this approach but a little differently.

Let’s make the reasonable hypothesis that “calm” air comes from the Airport itself and “windy” air is a mixture of Airport air and Airport_Suburbs air with mixing ratios k and (1-k). Here “calm” is used as an index to distinguish calm days as is “windy” (a common usage in R):

where “calm” + “windy” = t (time) i.e. they are are subsets. This represents the mixing on windy days.

Assuming that Parker’s calculations are correct and there is a greater trend on windy days as Joe observed, then this implies that there has been a a greater temperature increase in the Airport Suburbs than at the Airport itself. Not only does this make sense, but it’s exactly what one would expect from observed suburbanization and development around airports throughout the world during the past 50 years. The airport led development and there have typically been more changes in the surrounding suburbs than at the airport itself (despite continued development at many airports).

So has Parker proved that urbanization and microclimate problems have not infected the GISS,CRU and NOAA temperature composites? Nope. At most, he’s provided evidence that temperature increases in airport suburbs have slightly outstripped temperature increases at the airport itself. Is this a surprise? Nope. Does it show that Jones and Hansen and Karl were justified in not carrying out any quality control on their sites? Nope.

Another problem with Parker is that he just looks at wind speed, not wind direction.
Unless the distribution of heat is completely uniform, and the temperature sensor is in the exact center of that distribution, then the direction from which the wind is blowing will have an affect on the temperature at the sensor.

For example, if the sensor is near the western edge of your town. A wind from the west will cause more cooling than a wind from the east, as the wind from the east blows over a greater portion of the town before it reaches the sensor.

A wind from the north or south will have a different temperature affect.

As someone else has pointed out, whether the wind is caused by a cold front or a warm front will also affect how the wind affects the readings of the temperature probe.

There are so many variables not accounted for by this study, I just don’t see how anyone can take it seriously.

ie. calm days warm by the trend in calm-day temperatures plus the change in UHI. The trend of temperatures is allowed to be different on calm and windy days as you suggest. So your equation is rewritten:

Trend Ac – Trend Aw = (1-r) Trend UHI + Trend Tcmax – Trend Twmax

Where Tcmax is the calm-day max temps and Twmax the windy-day max temps.

So if Trend UHI is positive it has to be balanced by differences in the trends for Tmax on windy vs calm days if the UHI influence disappeared.
It implies that for stations unaffected by UHI, windy days should be getting warmer faster than calm days. Is there observational evidence for this, and does it sound likely?

#39 and #40 are fair points that embrace the Parker methodology if not the conclusions.

Parker’s implementation is a data-mining exercise looking for an interesting result. #39 sounds plausible (industry is attracted to airports), though it requires that suburbanisation increases uniformly which to me sounds a little unlikely.

#40 would be good for a secondary test of the results. Could the data be parsed into Westerly/Easterly days rather than calm/windy days. I guess the statistics get a lot more hairy given that wind direction implies different sources of the wind as well as different urban effects.

#41 I completely agree, though we’re obviously thinking about different people 😉

Re#22, purely anecdotal, but I agree with Johnson on the max UHI effect being observable a few hours after sunset. I spend a lot of time roaming my property and going to-and-fro the house to the fringes, and that’s exactly when I can notice the temperature gradient changing as I move towards or away from my brick home. I’ve never noticed this around dawn – any difference the temperature several hundred feet from the house is imperceptible to me (granted, I’m not a digital thermometer) from that adjacent to the house. I’m sure my brick home isn’t characteristically representative of everything that is a part of the UHI, but my personal experiences leads me to believe Johnson’s statement makes more sense. And I’m not sure how Parker can use Johnson’s statement to justify his own.

Let’s put it this way. Consider a rural location in 1900. Today it is on the outskirts of a town and comparing the site to still rural nearby sites, it is a degree warmer. One can then deduce that the UHI effect has had a 0.1 C/decade trend at that location over a century. Yet according to Parker’s analysis, this site has experienced virtually no net UHI warming.

The hypothetical site I described above is observed to occur in many places all over the world. It should be obvious that UHIs are contributing to the observed global warming. It can be better quantified with some direct field work.

As I pointed out above, Parker’s indirect approach is too simple and his equations are incomplete. That is why he fails to detect what has already been measured directly by many people.

#50
MarkW seems unable to follow the nuances of the discussion. I said:

If urbanisation is found where [Parker’s method] doesn’t detect it, it means either that the urbanisation change is unimportant (which backs up the temperature record), or that the windy/calm hypothesis is wrong.

That’s why more transects would be of interest to test the latter possibility. The windy/calm hypothesis has been demonstrated in some places, but maybe these places were exceptional for some reason.

I don’t think it is clear from the article that the Parker paper explicitly states that the UHI effect does exist.

Essentially it is saying that comparing trends of calm and windy nights suggests that the increase in the UHI effect is not significant over 50 years.

If an absolute UHI effect of a larger magnitude than now assumed (from Parker etc.) were demonstrated, it would, even without showing how much it changed over the last 50 years, indicate that the measurement of long term temperature trends might be better made by excluding urban areas and for that matter push up data users’ antenna for those environments of “rural” stations that might cause similar UHI effects.

I think Parker’s papers tend to minimize the temperature measurement uncertainties whereas the antithesis would allow for more uncertainties that might put more emphasis on the satellite and SST measurements and better insuring the accuracies and precisions of those methods.

Quick question: When trying to detect the UHI signal don’t you have to use raw daily temp data? It seems to me that, if station moves and other discontinuities like TOB are randomly distributed between urban and rural stations, that a correlation of population growth and temp would show magnitudes of temp changes relative to differing population growth rates. Subtracting Rural from Urban would show a crude UHI signal. The raw data are the only data that would contain that signal unequivocally. (Please don’t rehash the degree/radians thingy)

Steve you’re the one who claims that the temperature record does not show urbanization.
I can’t find ANY evidence to support such a claim, unless you are willing to use the highly adjusted temperature data created by the Team, using secret data and secret methods.

I guess your one of those guys who believes that nuance requires one to not see anything that doesn’t fit into the agenda you are trying to push.

#54
Read the Parker paper which aims to demonstrate that the effect of increasing urbanisation is small, not zero. In #51 I’ve provided what I think are the key things to double-check or improve that may falsify the hypothesis.

Parker’s paper is a novel and interesting piece of science with a reasonable base of science to back it up. Of course “novel and interesting” doesn’t mean “true”, but I’d rather have evidence rather than anecdotal reports of the UHI in Dallas today. So what? We know there is a UHI in Dallas today. How can we work out what the UHI was in Dallas 50 years ago? Then do the same for all the other stations.

I’m not some sort of Team acolyte who has utmost faith in the temperature record. On the other hand Steve McIntyre’s partial and somewhat misleading introduction to this paper gives me good grounds for questioning the opposing team.

Regardless of what conclusions were drawn about the 277 sites that didn’t show an ‘urbanization effect’, Parker did conclude that there is detectable UHI in communities as small as Barrow, Alaska. This is a community that is only accessible by airplane, snowmobile, and to a limited extent by sea. With a population less than 5,000, no manufacturing or agriculture, and a populace that to a great extent still lives a subsistence lifestyle it is the epitome of a rural location. And it is contaminated by UHI. Of course Hinkel et al show that very clearly, but even a team player has shown that the assumption of rural sites being free of any UHI contamination is very questionable.

I’m not some sort of Team acolyte who has utmost faith in the temperature record. On the other hand Steve McIntyre’s partial and somewhat misleading introduction to this paper gives me good grounds for questioning the opposing team.

Not sure what you have in mind specifically about Steve M’s handling of the Parker paper, but from your sensitivity you obviously can appreciate the questioning here of the indirect Parker approach that has somehow become authoritative in the world of climate science and apparently without the amount of scrutinizing it would appear to deserve.

Parker’s indirect approach reminds me of a paper on the effects of the minimum wage level on employment by Card and Krueger that used the indirect data derived from generalized interviews with minimum wage managers to show that increasing the minimum wage actually increased employment. That paper has, of course, become the authoritative document used by those advocating increased minimum wage levels even though it has faced a goodly amount of criticism.

I looked at Parker’s paper on this subject in Nature 2004.
His stations are from GCOS not CRU or GHCN.
I put some comments in Coolwire.
Scroll down one up from end of page.
I have just added the comments on huge heat transfers by winds.

A similar statement can be made regarding the paper by Quayle, et al, in determining the systematic differences between CRS and MMTS measurements which can be found here. In this paper the entire analysis was based on relating particular sites where the CRSs were replaced by MMTS instruments with some nearby sites which used the CRS housing continuously and then selecting that nearby site that showed the best correlation with the CRS record at the site where the instruments were changed.

It seems obvious that the right way to make a comparison would have been to have operated both instruments at the same site for a sufficiently long overlapping period to make the site comparison directly. For whatever the reason that wasn’t done, probably because the decision to compare the values was an afterthought which occurred some time after numerous sites had already been switched and the CRS devices for those sites were no longer available.

This proxy method might be regarded by some as valid on the grounds that it is comparing apples with apples, but there’s still a lot of difference been a McIntosh and a Delicious.

As for UHI data, perhaps one could compare the temperature trends of long-term, mature urban areas like NYC and downtown Chicago with more recent and rapidly developing urban sites like Phoenix/Scottsdale, Orlando FL, Las Vegas NV and Santa Fe NM., or the once rural suburbs of those mature UHI sites.

We also summarize the conclusions of our 2005 GRL paper on Climate Science [http://climatesci.colorado.edu/2006/01/23/why-there-is-a-warm-bias-in-the-existing-analyses-of-the-global-average-surface-temperature/] as follows:

“Readers of this weblog know that there have been comments on the warm bias that we have identified, as reported in Matsui and Pielke, GRL, 2005, with respect to the global analysis of surface temperature trends. This is an important issue as this climate metric is used as an icon to communicate the concept of global warming to policymakers. The abstract of the Parker 2004 Nature paper , for example, stated that the

“Controversy has persisted over the influence of urban warming on reported large-scale surface-air temperature trends. Urban heat islands occur mainly at night and are reduced in windy conditions. Here we show that, globally, temperatures over land have risen as much on windy nights as on calm nights, indicating that the observed overall warming is not a consequence of urban development.”

Parker 2004 has been used as evidence to argue that the global surface temperature trends are robust (e.g. CCSP, 2006). In the Matsui and Pielke paper, we show, however, that trends of surface air temperature should not be expected to have the same values for the different sets of days used in the Parker paper. Based on well understood concepts of boundary level meteorology, because Parker found similar trends, there necessarily must be some error in Parker’s analysis. For those unfamiliar with boundary layer meteorology, the reason for this is that minimum temperatures on calm nights should in fact show a larger warming trend than on windy nights (explained below), and not the identical trends reported by Parker. We were motivated to look at this subject because of the obvious inconsistency in the Parker results, and what we found has much broader implications for the long-term surface temperature record.

Studies of the lower levels of the atmosphere (lowest tens of meters) show that it cools at night when winds do not move warm air into the area. This cooling occurs as heat is lost to space. For this reason, minimum daily temperatures typically occur near sunrise, due to cooling overnight. The nighttime cooling varies with height. With light winds, the cooling is greater near the surface and less aloft, while with stronger winds, which are associated with greater mixing of the air above a particular location, the cooling rate is more uniform with height. Light and strong winds can be documented at a particular location from observed wind data.

The rate of heat loss to space is dependent on several factors, including cloudiness and the local atmospheric concentrations of carbon dioxide and of water vapor. Under cloudy conditions, the cooling is much less. Similarly, an atmosphere with higher concentrations of the greenhouse gases, CO2 and H2O, also reduces the cooling at night. Consequently, if there is a long-term trend in greenhouse gas concentrations or cloudiness it will introduce a bias in the observational record of minimum temperatures that will necessarily result in a bias in the long-term surface temperature record.

Because of changes to the atmosphere over the past century, there are several reasons why we should expect the nighttime cooling in the lower atmosphere to have been reduced. One reason for this is that carbon dioxide concentrations have increased, such that the local effect of greenhouse gas concentrations on temperature measurements is larger. Also, an increase of cloudiness has been reported which has the effect of reducing nighttime cooling. An increase in water vapor content in the lower atmosphere would also reduce the cooling rate at night.

Our paper shows that in such circumstances where nighttime cooling is reduced systematically over time, i.e., under trends of greater atmospheric greenhouse gases or an increase in cloudiness, the resulting effect will be to increase minimum temperatures from what they would have been absent the reduced nighttime cooling. This increase in minimum temperatures is greater on nights with light winds than nights with strong winds, due to the mixing of air, and can be on the order of 1 degree C in the lowest 10m above the ground. Minimum daily temperatures are of course important because they are used as input to calculate the daily temperatures that comprise the long-term surface temperature record.

When there is a long-term trend of a reduction in nighttime cooling, then when temperature data are collected, the combination of all of the minimum temperatures on light and strong wind nights will result in an overstatement of warming trends by tenths of a degree. (Note that this assumes that the overall reduction of nighttime cooling such as due to more cloudiness over time and/or increases in the atmospheric concentration of carbon dioxide and/or water vapor is on the order of 1 watt per meter squared. Based on the IPCC, 2001 findings, this is a reasonable estimate of the change over the recent decades in the atmospheric radiative forcing).

What this means is that because (a) the land surface temperature record does in fact combine temperature measurements of light wind and windy nights and (b) there has been a reduction in nighttime cooling, the long-term temperature record may be contaminated by a warm bias that accentuates the observed trend of warmer temperatures. Such a bias would be of similar or larger magnitude to those biases recently discussed in the context of global satellite measurements of temperature. The reduction in nighttime cooling that leads to this bias may indeed be the result of human interference in the climate system (i.e., local effects of increasing greenhouse gases or human effects on cloud cover), but through a causal mechanism different than that typically assumed.

This effect results from a systemic microclimate effect in temperature data which are present in the global temperature record, but are unaccounted for in current analyses. This raises the possibility that those GCMs that appear to accurately represent global average temperature trends over recent decades may be obtaining results that look right when compared to data, but for the wrong physical reasons. If so, this would call into question their ability to accurately predict the future evolution of the climate system.

The broader implications of Matsui and Pielke (2005), which will be well understood by anyone with an understanding of the physics of the lower atmosphere, should cause consternation among anyone who uses the global temperature trend record for scientific or policy purposes. As we have emphasized here (as have others, such as Hansen, Levitus, Barnett, Willis) a more meaningful metric than global average temperature to assess global warming is ocean heat content.”

The Parker 2006 is clearly inconsistent with our knowledge of both urban effects, and of stable nocturnal boundary layers.

SteveM the worthy ( thats my nickname for Milesworthy) Parker’s logic escapes me.
Open question. Could you lay it out. very simply. I think you have some points,
but I can’t wrap my arms around the bigger picture.

One of the main IPCC creeds is that the urban heat island effect has a negligible impact on large-scale averages such as CRU or GISS. The obvious way of proving this would seem to be taking measurements on an urban transect and showing that there is no urban heat island.

No! The study here is looking at temperature trends due to increasing urbanisation. More transects do not help. Parker explicitly states that he doesn’t dispute that UHI exists.

Steve has picked up on the fact that the Johnson citation says nothing about Tmin being affected by UHI. This means we have two possibilities:

1. Tmin is affected by UHI and Parker is right despite the fact that the evidence is not fully there.
2. Tmin is not affected by UHI – Parker is wrong. But then so is UHI. Therefore the warming trends are correct.

You can’t have it both ways.

So am I correct that you think that UHI exists, and that Parker doesn’t deny that, but you and Parker think that despite the population doubling since 1960, the effect of the UHI is unchanged? Am I interpreting this correctly?

“One of the main IPCC creeds is that the urban heat island effect has a negligible impact on large-scale averages such as CRU or GISS. The obvious way of proving this would seem to be taking measurements on an urban transect and showing that there is no urban heat island.”

No! The study here is looking at temperature trends due to increasing urbanisation. More transects do not help. Parker explicitly states that he doesn’t dispute that UHI exists.

Steve has picked up on the fact that the Johnson citation says nothing about Tmin being affected by UHI. This means we have two possibilities:

1. Tmin is affected by UHI and Parker is right despite the fact that the evidence is not fully there.
2. Tmin is not affected by UHI – Parker is wrong. But then so is UHI. Therefore the warming trends are correct.

You can’t have it both ways.

So am I correct that you think that UHI exists, and that Parker doesn’t deny that, but you and Parker think that despite the population doubling since 1960, the effect of the UHI is unchanged? Am I interpreting this correctly?

Pardon my inexperience in this field, but as far as I understand your comment, it seems to amount to saying that we could be fooled into believing in an enhanced greenhouse effect – by the enhanced greenhouse effect.

re #67
My read was that the effects might be strongest in the lowest few meters of the atmosphere, where we happen to take the temperature measurements at the land-based stations. This would exaggerate our perception of what was going on, and maybe mislead us humans on what policies to follow. just my 2 cents.

As I understand the thrust of Parker’s argument, which I have re-stated in #15, I think there are two points that are still persistently not quite universally grasped:

a) Parker is NOT saying that there is no urban-heat-island effect. He IS saying that a UHI effect should be detectable through the Ac-Aw measurement, and by the Bc-Bw measurement. (Using my own terminology from #15)

b) Therefore, if there is any kind of trend in the UHI, it should also show up as a trend in the Ac-Aw and Bc-Bw measurements.

Another question that is commonly posed: Why is Parker using these complicated subtraction approaches, which require division into windy vs calm days, instead of directly measuring the UHI effect as Hinkel did?

There are two reasons I can think of for that:

a) Parker is doing an analysis of data already accumulated over some 50 years. We have no time machine to travel back over those 50 years, so the only way we have of studying them is to use what we already have.

b) This particular technique allows us to compare data at one site, over time. That removes some dimensions of concern with respect to comparing data at different sites.

And, finally, using more direct methods is a different experiment. No one is being discouraged from doing that as well! And Hinkel et al. have done just that, for one location, Barrow, AL. But we don’t have any other data. So we need to get the most use out of what we actually have.

Various concerns raised about Parker’s perhaps mistaken interpretation of Johnson’s paper in my opinion miss the point that these remarks were mostly contextualization, and do not affect the main thrust of Parker’s argument; which I think is presented more straightforwardly in #15. (Of course, I would, wouldn’t I?)

There is the interesting suggestion from Steve McIntyre that the higher trend in the windy-day signal implies a mixing-in of warmer air (due to suburban heating up) that over-powers the UHI (for example, from a relatively static airport) and thus suppresses the growing UHI.

It seems to me that this is the sort of question that is too difficult to answer by looking at aggregate data, because it could apply to very specific situations somewhat differently: It would be more fruitful to ask Parker to address the question directly using his site-specific data.

You suggest that there would be a difficult distinction to draw between “windy” and “calm” in Colorado: 40 mph vs 20 mph.

Parker actually uses percentiles, so in principle that does not cause him classification problems.

However, as a matter of physics, I would agree with you that the more important factor should indeed be the speed. Once the UHI has been removed by a fast-enough breeze, I should think that it would be gone, and I would think that it can’t get more gone.

Anthony Watts (#16) and Willis Eschenbach (#26) have expressed concern at all the proxying required to do get the temperature and the wind speed onto the same time-space point.

Obviously, it would be better if one didn’t have to do this. However, there are two reasons for thinking that this does not wipe out the usefulness of that approach:

– Spatially and temporally, there will be a degree of autocorrelation of behavior of wind and temperature. So even if the match is “off” a certain amount, it is reasonable to expect a degree of correlation.

– As this correlation weakens, I think the first-order effect would be the reduction of the Ac-Aw and Bc-Bw signals, because of misclassification of calm and windy days. But these signals have both survived: they are definitely well above 0, the value one would expect if the assignment into windy and calm were random. Since the signals have survived, I think it is also reasonable to expect that the trends in these signals have also survived. And the Parker hypothesis turns on this trend.

aurbo (#61) has suggested that it would be easier to compare the UHI trends of Chicago (for example) with those of Santa Fe.

The problem is that you are then comparing the trends and behavior of two cities with different geography, economy, climate, etc. You end up trying to account for two different changes in time and one change in space. Messy.

The relative simplicity of Parker’s approach is that you are fundamentally comparing data from one location: the Ac-Aw and the Bc-Bw signals. It’s not foolproof, but the dimensionality of the number of problems in interpreting it is far less.

Douglas Hoyt (#37) pointed out that there could be a correlation between degree of windiness and temperature. He tries to take this into account by modifying my Eqn. (6) (from #15) from:

Eqn.(6)
A(j,n) = Tmax(j,n) + r UHI*(j,n)

to Eqn. (6′)
A(j,n) = r’ Tmax(j,n) + r UHI*(j,n)

However, I agree with Steve Milesworthy (#42) that this is not the correct approach.

The point is that Tmax(j,n) is the actual “real” temperature of an ideal weather-station that was not being afflicted by asphalt highways, burning cans of rubbish, etc. It happens not to be available (or we would be using it!), but that is the intent. If on the other hand the day is windy, and by natural correlation that turns out to imply a cooler day, that lower temperature would be the starting point for both the actual measured temperature Aw(j,n) and the ideal temperature Tmax(j,n): There is no reducing factor. If it’s colder, it’s colder.

So how would the correlation between windiness and cooler temperature be reflected in the signals and the trends? I would expect that it would increase the Ac-Aw, by reducing the temperature on windy days (Aw); and reduce the magnitude of the Bc-Bw signal on windy nights (Bw)(because Bc is generally less than Bw, due to the near-surface temperature inversion).

If there is no trend over time in the windiness/temperature correlation, it won’t make any difference to the relationship of the UHI* trend to the calm-windy signal trend. I don’t, however, see an immediate way of disentangling it from the NSTI of Eqn.(8) / #15.

Paul Linsay (#19) and Mark W. (#40) both point out that a correlation between wind direction and temperature would have an effect.

After some thought, my conclusion is that it will weaken the signals. However, as with the question about proxying (#76), as long as the signal survives, the trend is still meaningful. If the signal is non-zero but the trend in the signal is zero, that tells me that either the UHI is not increasing, or else there is a terrible conspiracy on the part of nature to convince me that it is not increasing.

Neal, thoughtful responses. I don’t think that you quite got the nuance of my suggestion though. You said:

There is the interesting suggestion from Steve McIntyre that the higher trend in the windy-day signal implies a mixing-in of warmer air (due to suburban heating up) that over-powers the UHI (for example, from a relatively static airport) and thus suppresses the growing UHI.

It seems to me that this is the sort of question that is too difficult to answer by looking at aggregate data, because it could apply to very specific situations somewhat differently: It would be more fruitful to ask Parker to address the question directly using his site-specific data.

I’m not saying that the air in the suburbs is warmer than at the airport or that it overpowers the UHI effect. Quite the contrary. It would be my view that the airport would continue to be warmer than the adjacent suburbs. My suggestion is that the trend in the suburban area surrounding the airport may well be slightly greater than at the airport proper due to a greater rate of urbanization change in the adjacent suburbs than at the airport itself (once the airport is built). Empirically this would be the case in Toronto and in many cities that I’ve traveled to in my life.

This suggestion appears to me to skin all the available cats. The rate of urbanization in the adjacent suburbs can be greater than at the airport at all times without the suburbs ever catching up to airport microclimate urbanization. Because the trend in the adjacent suburbs is higher, the trend of a blend of airport + adjacent suburb air will be greater than the trend of airport air by itself, while at all times being less than airport air. Thus the trend of windy days would be greater than the trend on calm days (assuming that Parker’s calculations are correct.)

It’s not up to me to prove the opposite – it’s up to Parker to consider and eliminate this fairly obvious possibility.

c) In most cases, there appears to be no trend in the (calm-day) – (windy-day) signal.

a), b) & c) combined imply that the urban-heat-island effect is NOT increasing on a global basis, and therefore CANNOT serve as the “real” cause giving rise to the impression of global warming as an artefact.

So you can conclude that:

– global warming is real; or
– it’s an illusion – but it’s an illusion not caused by an increase in UHI effects

#81 Steve
A neat and clear explanation. The explanation also makes a great case for carefully ascertaining both UHI and microclimate effects associated with changes at a location and of locations (not to mention various appropriate and inappropriate corrections). It also points to the fact that the measured effects are still small and can be produced by these non-global artifacts. At a minumum the research should be in place to eliminate them.

Basically, I think by selecting airports, Parker misses most if not all of the causes of UHI
propsed by Johnsen et al. Not that a airport is rural, not that there is no concretee,
But the canyon effects are mssing, the multipath reradiation effects are missing. Shelter
is missing, and building close to sensors are missing.

So, he picked sites with no substantial UHI causes. then found out there was no difference in Tmin trend.

We might be able to get an idea of the magnitude of the effect on global temperatures of the potential errors in land-surface measurements being discussed by comparing land and ocean temperature trends from different sources. NCDC ocean surface measurements yield a trend of 0.124 degrees C per decade from 1979 to 2006, while UAH MSU lower-tropospheric measurements over the ocean yield an almost identical 0.127 degrees per decade. Ocean surface temperatures are determined from ships and bouys, tied into satellite infrared measurements of ocean surface skin temperature, while MSU measurements use microwave emissions from the atmosphere, so that these measurements can be considered to be essentially independent. These measurements cover 70% of the earth’s surface. Over land, NCDC surface-station measurements yield 0.309 degrees per decade for the same time period, while GISS land stations yield 0.188 degrees per decade. On the other hand, UAH MSU lower tropospheric measurements over land show only 0.166 degrees per decade – 53% of othe NCDC trends and 88% of the GISS trends. By far the easiest way to explain this difference is that the land surface warming trends are too high, by a factor of 13% or more, for many of the reasons discussed in the other posts.

Well, I’m enjoying the debate in this thread. While Neal and SteveM-worthy are clearly on the warmer side, they’re holding their own and making good points, though they’ve not convinced me yet. But I do suspect that Neal, at least, is not an amateur on the subject. Either I don’t recognize the name or he’s here incognito. Which is fine with me, but until I’ve had a chance to look closely at his post 15 I’m sticking to the sideline.

Lighthouses would appear to an excellent source of data free from UHI and other biases. And lighthouses will move very infrequently, so a guaranteed stable location. Also, many lighthouses are (relatively) close to ports = urban centers for comparison.

I finally got around to doing a back-of-envelope calculation on the direct UHI impact — what we know “for sure” about the impact of urbanization — for the 6000-acre suburban-U.S. community into which I and 75000 other people have moved during the past 45 years. I was a bit surprised by the result: The average power-per-unit-area introduced by urbanization would be 75000[people]*1390[watts/person]/(6000[acres]*4046[m2/acre]) = 4.3[watts/m2]. By coincidence (??), four watts per square meter is the AGW-hypothesized increase in net radiative forcing one gets by doubling atmospheric CO2.

Clearly this BoE analysis is greatly oversimplified. Among other things, it ignores: heating due to changes in albedo; “waste” heat (which may be several times greater than power consumption); convection and advection; etc., etc. Actually solving the problem “correctly” — using first principles — would be hard because of boundary conditions and parameter estimation (too many coefficients for me to be comfortable, anyway).

Normally one could settle this quickly by looking at the data — exactly what Christy (#23) has attempted to do — but that only works if the data were collected with sufficient care. Not so sure we can say that…

In conclusion, it is hard to feel confident about any particular explanation for “observed warming” when serious questions remain about whether it might be no more than an artifact of changes in sampling protocols and UHI.

An obviously inebriated fellow is seen crawling around on his hands and knees under a street light. When asked what he is doing, he replies that he is looking for his car keys – which he dropped while trying to open his car. When asked where his car is, he points halfway up the block. When asked why he is not looking for his keys there, he replies that the light is better under the street light.

While I will not suggest that Parker was inebriated, I do think Parker’s methods on this study have a lot in common with that fellow under the street lamp.

Parker chose to look for UHI effects (his keys) by using airport (street light) temperature data, because the data are better – ignoring the fact that UHI effects he is looking for are not found there (illustrated by Steve Mosher in#85). Now, granted there may be some small UHI effect by winds blowing UHI into the airports (Steve McIntyre in #81), but even that effect is impossible to sort, because Parker effectively wrapped his head in gauze and put on dark glasses when it came to his use of wind data (Willis Eschenbach in#26). Station wind data exist, why was it not used?

I suspect that the fellow under the street light did not find his keys under the street light and would be surprised if anyone found that conclusion to have scientific significance (except to illustrate bad logic). Parker’s methods likewise were not designed to find his keys (UHI effects trends), so why does IPCC or anyone else think that Parker’s conclusions have scientific significance?

The joke is funny because of the logical disconnect, but I find that same disconnect in the Parker study (peer reviewed, published in Nature) more than a little sad.

Well, A lot more seems clear to me now ( that’s probably a bad sign)
Neal and all the steves have helped

As Keith points out in #87 the odd man out is the land surface record. Not by much
other records show warming but the land record is the odd man out. Wouldn’t that
be the natural one to bring in line?

And I look at some of the sites ( Marysville for example) and the sensor
has been put into an IR corner reflector and topped off with a C02 Lid.

When We measure SST I would NOT expect to find hot water corner reflectrs.
Same thing with measuring the troposphere. I would expect a diffuse and
and well mixed medium. Few local hotspots. ( I probably should have read
something before speculating)

On the surface of the globe, there are plenty of ways to create little local
hot boxs. Metaphorically like a corner reflector, or IR concentrators.
Pure conjecture on my part here. On problem though, s that one would expect
the trend in the hotspot to match the trend outside the hotbox (hmm maybe not)

Also, one other thing bugged me about parker and that was the characterization
of winds speed by tercile.

My understanding (ha) is that UHI would be present on calm nights because of the
lack of mixing. Think of a box with a Lid of C02, Radition goes in, bounces around
and is delayed in coming out. TMin registers higher. If yu have turbulant flow then you
get mixing, lid comes off, IR escapes more rapidly. Anyways, the problem
with characterizing wind in terciles and not velocity May be related here. Also,
the local area, size of surrounding buildings, trees, leaves on trees, etc will
all change the vecolity at which the flow becomes turbulant. How does one
calculate a reynolds number for a sensor site? Maybe this is what Roger is pointing
to.. I havent read his stuff yet..I’m just musing for now.

Not many have commented on Pielke’s comment #62. As I understand it, he is arguing that, if the greenhouse theory is correct, “minimum temperatures on calm nights should in fact show a larger warming trend than on windy nights” and thus the finding of identical trends contradicts the greenhouse theory. While this does not address the UHI issue as such, it is a significant point that other readers of the Parker paper seem to have missed.

The other significant point Pielke makes is that “minimum daily temperatures typically occur near sunrise, due to cooling overnight.” This point has also been discussed at some length by Jonathan Lowe. Jonathan has shown that, for all the locations in Australia that he has looked at, while the minimum temperatures have shown an upward trend, temperatures at 3am, for example, have not shown any significant trend. Jonathan notes that the minimum temperatures occur shortly after sunrise before the heating from the sun begins to raise temperatures again. He also looks at trends of temperatures measured each day at other times of the day. What he finds is that the trend is larger when the effect of the sun is also likely to be stronger. Jonathan concludes that the trend in temperatures measured at different times of each day is evidence of increased solar heating in recent decades rather than greenhouse warming.

Neither of these arguments address the issue of whether UHI is affecting measured minimum temperatures, but they may in fact be more important points to make about measured trends in minimum temperatures than the one Parker is trying to make.

When we’re discussing Parker’s “demonstration” on UHI, remember that Parker’s previous claim to fame was his co-authorship of the Folland and Parker bucket adjustment see CA discussion and references here.

LOL – Not only the same story, but applied to the same guy.
Willis Eschenbach:

A further oddity. The HadCRUT2 error analysis says:

Blending a sea-surface temperature (SST) dataset with land air temperature makes an implicit assumption that SST anomalies are a good surrogate for marine air temperature anomalies. It has been shown, for example by [Parker et al., 1994], that this is the case, and that marine SST measurements provide more useful data and smaller sampling errors than marine air temperature measurements would.

This jump of logic reminds me of the old story about a man standing under a street lamp looking on the ground. His friend says “What are you looking for?”.

“My keys”, he replies. “Oh, did you lose them here?”, the friend asks. “No, I lost them over there in the bushes … but the light is much better here.”

Their justification for using SST in place of air temperature is that there is “more useful data and smaller sampling errors”?!?? As a long-time sailor and commercial fisherman, I can assure you that sea and air temperatures are often quite different. This is particularly the case in areas where a change in SST causes the formation of fog, since much of the sea temperature change is taken up, not by a corresponding air temperature change, but by the condensation of water.

I see that the last twelve years have done nothing to improve Parker’s sense of logic.
Cliff

I’m starting to plow through Pielke, the second paper is 91 pages. It’s much easier
for us to read Parker’s data mining excercise and raise objections. Pielke requires a bit more
understanding. Not that parker is a slouch, but rather Roger’s second paper that he links
brings to bear a lot of issues. So, it takes more time.

I have this initial observation.

After looking at 100 year long daily records of Tmin and Tmax for a bunch of sites, it seemed pretty clear
to me that we could do with one measure. That caluclating a Mean temp ( Tmax+tmin)/2
was unneccessary for determining a global land temp TRENDLINE. Basically, If Tmax is going up,
Tmin should ge going up, Tmean will go up, and all slopes will live in the same neighborhood.
( something fun to test)

One measure, Tmin or Tmax seemed just FINE.. for the trend. The only question I had
was WHICH ONE? and was there a physical basis for picking one.

Which measure was more likely to be skewed by site selection ( sample selection)?

I dunno. Anyways, a couple pages into Peilke I read this:

“In summary, given the lack of observational robustness of minimum temperatures, the
fact that the shallow nocturnal boundary layer does not reflect the heat content of the deeper
atmosphere, and problems global models have in replicating nocturnal boundary layers, it is
suggested that measures of large-scale climate change should only use maximum temperature
trends.”

Might be interesting to look at how the varaince of Tmax compares to Tmin. Might be
interesting to do anomly maps for both..

RE95, it seems to me that somebody needs to create a 3D near surface temperature station model to test various microsite effects. Seems like a perfect task to do on a Silicon Graphics Box. Wind, boundary layer, structures, trees, bushes, asphalt, heat sources, etc could all be applied over a time series that has changing insolation based on the solar illumination pattern for a given lat/lon.

Has anybody done this? While I own an SGI O2 I don’t have the time at the moment as I’m full up managing two projects.

The funny thing is that despite reading this website for a year now I was still surprised by this revelation. There really is a relatively small community that is responsible for 90% of the research behind the AGW consensus.

I am as skeptical as anyone on AGW, or at least exaggeration thereof, but I still tend to browse the next paper as perhaps it was written without an agenda. I am sure the general population views climate science that way. They are just scientists, what agenda could they possibly have?

If you are researching the adjustment of temperatures based on bucket types and have to bring wind factors to show how minimal UHI is, you have an agenda. If someone wrote a paper using wind to show that AGW was not happening, or is minimal, and I am sure with all the variability available that could be done, the AGW community would go apoplectic.

Anyways, you guys thought I was kidding when I said we should scale down a GCM to microsite
sizes. When I read the “canyon, boundary layer, multipath” stuff in Johnsen. I got the same
Idea. ( past life long ago and far away)

After 50 years of participating in taking and recording temperature observations (mostly at CRS equipped sites) I have made the following observations and some tentative conclusions: Every site has its unique micro-climate idiosyncrasies. These stem from a diversity of site-specific conditions, including, but not limited to: local vegetation; presence of building structures and contributions made by such structures involving energy use, heating and air conditioning, etc; exposure to winds, the wind velocities determined by climatic factors and also whether certain wind directions are more favored than others by terrain or the presence or absence thereof to bodies of water; proximity to grass, asphalt, concrete or other material surfaces; the physical conditions of the CRS itself which include: the exact location of the temperature sensors within it, the degree of unimpeded flow of external air through the CRS, the character of the paint used; the exact height of the instrument above the external surface (noting that when the ground is covered by 3 feet of snow, the temperature instrument is about 60% closer to, or less than 2 feet, above an excellent radiating surface, much closer than it would be under snow-free conditions).

Thus, determining long-term climate trends from CRS data almost demands that individual trend lines be generated for each site.

As to whether to use the max, min or mean trends, any of these would probably do as long as the (linear) trend-lines are parallel, but again this would require that trends are calculated for each site individually.

At a given location, the near surface air temperatures are determined by three principal processes; advection1, conduction and evaporation/condensation of water. Direct heating or cooling by radiational effects within the air parcel itself is usually negligible2. On calm nights in the absence of liquid water (precipitation or puddles) at the site, the air is cooled by conduction from the ground or other proximate solid surfaces…(e.g. foliage). The cooling or heating effects from incoming or outgoing radiation occurs on these surfaces and not to any appreciable effect within the airmass itself. Little wonder why the physical properties at and near the site are of such importance.

1. Convection is not included as it is a special case of advection.
2. On occasion, when the lower troposphere is filled with particulates, a combination of absorbed radiation by these particulates and conduction to the adjacent air molecules, can heat the air significantly. See for example the remarkably high max temps that occurred over parts of WA, ID, OR and MT downwind from Mt. St. Helens shortly after the eruption of May 18, 1980.

You proposed to explain Parker’s null result in seeking a trend in calm-day vs. windy-day signals by assuming a windy-day mixing of non-local (Suburban) air with the local (Airport) air. I am going to try to formalize this proposal, using the same terminology already developed in #15. I will have to (extend it slightly.

The indices (j,n) are defined as in #15, but I will suppress them for the time being:

For calm days, there is no Airport/Suburban mixing, so Eqn.(5) from #15 is unchanged:
A = Tmax-a + UHI*-a

For windy days, I have to weight the two equations corresponding to Eqn.(6) for the two locations by weighting factors, k and (1-k). Thus, for windy days:

A = k(Tmax-a + r UHI*-a) + (1-k)(Tmax-s + t UHI*-s)

[Here, r is as before the windy-day reduction factor for UHI. t is similar, but reflects the fact that the temperature of the air transported from Suburbia to the Airport will be higher due to UHI – but the effect could be diluted. So I introduce a parameter “t” to avoid forcing them to be the same, which could lead to unphysical results.]

Ac and Aw are defined as the interpolated extensions of the calm-day and windy-day values as before, and are defined for all days:

Under the assumption of (more-or-less) uniform GW, the TREND(Tmax-a – Tmax-s) should also be 0; and under the assumption that GW is not happening, it is also 0, so we’re covered either way if we assume it’s 0. So let’s.

That means we have the equation:
0 = (1-rk)TREND(UHI*-a) – t(1-k)TREND(UHI*-s)

So that’s great, there is a value of the windy-day mixing ratio that would ensure:
a) Compliance to Parker’s finding that Ac-Aw has no trend; and
b) Still allow the UHI* trends to be non-zero. As Steve McIntyre pointed out, it must be that r(TREND(UHI*-a)) > t(TREND(UHI*-s)), so that there is is some warming over time from the suburban increase in UHI; and
c) There can still be the observed UHI, provided
(1-rk)UHI*-a > t(1-k)UHI*-s

But there are two pieces of bad news:
(1) This works at only one value for k:
k = (TREND(UHI*-a) – t(TREND(UHI*-s)))/(r(TREND(UHI*-a)) – t(TREND(UHI*-s)))

So in order for this to be an explanation of the null-measurement of the increase in UHI over 50 years, the wind behavior has to choose the right value of the mixing parameter for the whole period. Quite a coincidence!

2) I said I was going to suppress the indices. But now I bring one back: i, the site number. In the equation above, all of those trends, and even the value of r and t, depend upon the site. That means that the right value of k really needs to be written as k(i). And the real meaning of that is that the miracle of (1) has to be repeated 277 times, independently.

What this says to me is that Steve McIntyre’s proposal to explain away Parker’s null result for the UHI increase is not plausible.

I’d say Parker got a null result because he was looking at the wrong parameter. I challenge you to observe a UHI at dawn. Typically large UHI occur at hot summers with clear skies, therefore the outgoing radiation is also not limited by clouds and towns cool rapidly and at dawn hardly a temperature gradient is remaining with the countryside.
During my student days I studied in Utrecht and lived in Zeist and I had to cycle past De Bilt. In the evenings I would feel the cold when leaving Utrecht, in the mornings I did not notice a difference.

These comments suggest that Parker’s study is flawed by a choice of sites that lacked the causes for UHI, and thus lacked UHI.

However, these comments miss the fact that in virtually all sites, Parker did find a UHI. What he did not find (except in 13 out of 290 cases) was any sign that the UHI was increasing over a 50-year period.

I haven’t followed the discussion in the past concerning the water buckets. I do see a common theme, however: An interest in trying to find a way to make use of the data that have already been collected over long periods of time. We are never going to be able to reproduce those measurements: There’s no time-machine. So whatever the difficulties, it makes sense to see what can be found out from these data.

Parker does address the issue of cloudiness in Appendix D. However, it is also difficult for me to tell what he managed to get out of it. It seems he didn’t have any direct cloudiness measurements related to the data that he was working with. What else can he do, except leave it open as a point for further study?

If the NSTIs are not trendless, this should show up in the Bc-Bw signal but not the Ac-Aw signal, so their trends will differ by that amount, instead of matching as they do in Eqn.(12). (You can start off with my Eqn.(10), but forget Eqn.(11).)

There are two effects to consider:
– the UHI: This is indeed reduced on windy nights, because the air gets mixed up.

– the Near-Surface Temperature Inversion: This is due to the fact that the ground cools at night due to IR radiation up towards the sky (only a small fraction of which is going to be trapped in the 2 or 3 meters it takes to pass the weather station), whereas the air will lose heat far more slowly. The result is that the ground becomes cooler than the body of air immediately above it, and this creates a temperature gradient that is in the opposite sense as that created by the UHI. However, just as with the UHI, it is weakened by the wind.

Using my terminology from #15, this shows up in the Bc-Bw signal, but not the daytime Ac-Aw signal: there is no NSTI in the daytime.

No, I think it was not in 277 individual cases. Even in some geographically aggregated cases he found what he considered to be statistically significant values for TREND(Ac-Aw). Unless I misunderstand your point or the paper.

I will comment more in detail later ( my brain is down another path now)

I’m having problems with Parkers title and your comment.

As I take the claim now, it is that UHI exists. Parker measured UHI.
But UHI hasnt increased in the past 50 years?

And Prior UHI was due to? And those conditions have remained stable?
Shown where in the paper?

So if UHI hasnt increased in the past 50 years, then UHI cannot be do to population
growth in the areas of the 290 stations.

And if UHI hasnt increased in the past 50 years, then we have not seen local
increases of C02 in urban areas. or if we have, those increases have no impact.

And if UHI hasnt increased in the past 50 years, then we have not seen increased
wind sheltering in these areas.

So, I’ll simply agree with Parker. UHI exists. but hasnt increased. WHY does it exist.
Cant be population or poulation density. those have increased. Cant be “urban greenhouse”
cause urban levels of GWG have icreased. Cant be buildings, cause that has increased.

So, if UHI exists, and If, as you say, Parker shows this. Then cough up the cause!

OK, I will reread his article. You have been most patient and understanding
while folks try to follow the logic ( thats not a criticism I have the same
problems with SteveM and RogerP, I’m just slow )

For grins I picked 3 sites we have good evidence for.

One in the middle of a feild. One a couple miles away in a small town.
The last 25 miles away located in a parking lot.

I looked at TMin from 1983 to 2006 on a daily basis.

Feild versus small town. Small town was about .3C hotter. TRENDS in TMIN
walked together down the time series like two lovers.

Then I look at the Feild versus The growing urban area. 20 miles away. Tmin delta was higher
( well UHI and microsite?? )
But the trend was increasing? hmm

So, there is an anecdote. Means nothing. If I got paid to do this stuff

Well, going with lighthouse data only would avoid the issues of UHI. But it will also confine your data collection to a very small set of specific situations: coastlines.

Anyway, what the Parker study shows is that complex situations offer a chance to get a look into the phenomena of UHI, NSTI, etc. Climatology is a science itself: It’s not just about whether or not global warming is going on.

You are right, I over-interpreted: What he found was zero trend, when averaged world-wide.

That is problematic if you want to explain the increase in global average temperature as due to UHI only, but it doesn’t require perfect cancellation everywhere.

However, it still requires that the value of the k(i)’s each be such as to provide something so that the UHI trends can be averaged to 0 globally: Still a kind of “conspiracy of nature”. Whatever it is that we are trying to explain cannot be as complicated as this.

”
Re use of airports.
If the lowest temperature of the day is measured just after sunrise, and the highest temperature is measured in the afternoon, then both temperatures are measured during active operating times for commercial airports. Might I suggest that there is air mixing from the planes even on “calm” days. Also, this anthropogenic mixing, to the extent it is a factor, is an increasing factor over time, due to increased air traffic. Sorry for the small size of the graphic, but 2000 air passenger traffic is at the very least 30 times 1950 traffic, with impacts ranging from relocation of airports, much more frequent takeoffs and landings, much bigger planes and jet engines. This increased mixing over time would over time cause the calm days to more closely match the windy days.

If the data is from airports, local wind data is collected, and was in 1950. Probably the more recent years would be computerized and could be compared with gridded data used by Parker to verify that there is a positive correlaton between the two (local and gridded) as he assumes..

But how can Parker take a general statement and apply it to data collected worldwide?

That might be appropriate for someone to try to explore further implications of Parker’s study, by extending it. But that’s not a limitation of his methodology or conclusions.

I assume that if there is a global trend in cloudiness that has come to R. Pielke’s attention, it would consist of data rather than a general statement. Hard to incorporate general statements into a research paper, easier to incorporate data. If the data on the trend exists and was known at the time the paper was written it would IMHO have been appropriate to mention it. Particularly given the explicit discussion of cloudiness.

I do think that a general trend in cloudiness would be a limitation on his conclusions. More tmin perhaps than tmax.

As Joe Ellebracht pointed out, I had over-simplified the conclusion: not every UHI was static, but the global average was. This makes it difficult to attribute the increase in global average temperature to an increase in the UHIs around the world.

The commonest explanation for it I’ve heard is from use of energy, for example, for heating. However, I can imagine that a city can grow without necessarily becoming more dense. Maybe you get a 1-degree heat island that grows from one mile in radius to 10 miles in radius. Will the weather station at the center pick that up? No.

Your field/town/city examples illustrates the point:
– You are able to directly measure the town’s UHI (which holds static at 0.3 degrees) by comparison with the measurements in the fields.
– Likewise the city’s UHI, which is growing.
– What Parker’s study says is, “You can measure the UHI in the town and in the city without using the measurements in the field. Just look at the Ac-Aw signals in the town and in the city: that already tells you the UHI.”

The commonest explanation for it I’ve heard is from use of energy, for example, for heating. However, I can imagine that a city can grow without necessarily becoming more dense. Maybe you get a 1-degree heat island that grows from one mile in radius to 10 miles in radius. Will the weather station at the center pick that up? No.

Doesn’t it depend on which way the wind blows? Esp. for airports which try to be at the edge of cities at least in the beginning.

Thanks for the compliment, but I’m definitely an amateur with respect to climate science. However, I have a pretty decent background in theoretical physics, and have done a lot of reading recently: some articles and some textbooks.

So, I make no special claim to expertise or authority, but I do try to do my reasoning “on the page”: What you see is what you get, except that I might toss in references to sources that can legitimately considered authorities, if I can’t avoid it.

To understand what I’m trying to do, you would look at #15 and #108, with an additional point at #122: I’d been so focused on the methodology that I wasn’t paying that much attention to Parker’s results!

I will have to read carefully (and slowly) through Pielke’s papers on the boundary-layer issue. That will take some time…

Might be interesting to look at how the varaince of Tmax compares to Tmin. Might be
interesting to do anomly maps for both..

After the publication of Parker’s paper, I communicated with him. I asked him about Tmax and why he used Tmin only. In response, he said that he left out Tmax data to comply with the journal’s request to keep his paper brief. He also sent me his Tmax plot.

When I compared Parker’s Tmax and Tmin data, it showed that the diurnal temperature variation has been decreasing.

Parker does not provide a proper statistical analysis of all the stations. We don’t know how much variation there is between stations. It’s hard to figure out why these particular stations were chosen. It’s like too many Team publications – no selection protocols are described. Here’s a plot of the available information for the full year from Table 1 together with a line fitted to the few points. Is there anything miraculous about this fit? Obviously not. There’s nothing in this information that is inconsistent with airport suburbs urbanizing.

BTW I would also express the differential a little differently than Neal. I think that there are two sorts of effects at work -there’s a type of “Canopy UHI” denoting whether the Airport has been folded into the urban dome and just how far; and a “Microclimate HI” which denotes effects particular to the Airport.

IT just doesn’t seem surprising to me that trends at the Airport would be similar to trends at Airport_Suburbs plus or minus a little. Neal, notwithstanding your equations, Parker hasn’t shown anything anything other than that and, as Joe observed and you concede, there’s nothing miraculous here or even surprising. You do have to watch the pea under the thimble.

Thanks for commenting and making me think a little more. I find the read/discuss/read cycle very educational and as someone else pointed out the civility of your responses sets an excellent standard.

A number of recent threads on CA have demonstrated pretty poor data collection conditions. Perhaps you missed these. You do seem to acknowledge that the data may be bad –

“So whatever the difficulties, it makes sense to see what can be found out from these data.”

My point is exactly the opposite of your conclusion. Whenever you have bad or suspect data, and it is possible to go get good data, I think you should do exactly that. For the purposes of evaluating UHI, I don’t see the need to look back 50 years. You just need a large enough sample of good controlled data. I do not believe that the long term data is either good or controlled.

Now based on the above, lets look at the title of the subject paper, that also sounds like a conclusion –

“A Demonstration That Large Scale Warming Is Not Urban.”

If that is the ultimate goal, why not go collect some decent controlled data and prove it, as I suggested back in #18? I offer that that gets you to the simple sure answer, not the roundabout debatable one.

However, I can imagine that a city can grow without necessarily becoming more dense. Maybe you get a 1-degree heat island that grows from one mile in radius to 10 miles in radius. Will the weather station at the center pick that up? No.

That’s a possibility, but empirically the “canopies” are bigger for bigger cities. I’ve mentioned before that maybe we’d be better off with weather stations at the center of the cities. But that’s not what Parker studied or what’s in the CRU and GISS systems (that are being updated.) You’ve got airports, which by and large, as I’ve said on many occasions, have started at urban edges and, in many cases have been folded into the urban canopy over the past 50 years or are incipient. How severe a problem is this? The only way you can tell is by old-fashioned quality control – checking every airport. There are no short cuts to quality control and Parker hasn’t invented one.

– I think the point for the global average is that you cannot attribute the increase in GAT over the last 50 years to UHI: That would show up, and it doesn’t.

– With regard to this graph in #132: What message are you getting from this? What does this line indicate?

#131: John Norris,

– The problem is that you can’t go back in time 50 years: What’s been done’s been done, and what hasn’t hasn’t.
– There is nothing stopping someone from setting up a new Barrows-like project in every major city in the world – except money. But that doesn’t happen to be what Parker did. He’s not stopping anyone else.
– By modern standards, the data that Parker had to work with are somewhat problematic. That doesn’t mean they are valueless. If someone gave you a suitcase packed with gold, and then remarked, “By the way, the interior of the suitcase has been completely contaminated with arsenic.”, would you abandon the suitcase? Or would you expend some effort seeing if there were some way of getting some value out of a unique resource?
– And by the standards of science 100 years from now, the data we have collected on climate will seem hopelessly imprecise. Does that mean we shouldn’t bother to collect any?

Neal, we’re spinning here, but I’ll try one more time. I’m not saying that temperature increases in the past 50 years are due to UHI; I’m saying that Parker’s analyses don’t constitute a “demonstration” that they aren’t. (Also note that Parker has avoided the 1930s. This type of decision always has to be cross-checked with these guys.)

Hypothesis 1: airports and airport suburbs have had a more or less commensurate increase in canopy UHI + Microclimate heating artifacts i.e. observed increase is nonclimatic.

Hypothesis 2: airports have had no increase in canopy UHI or microclimate heating artifacts i.e. observed increase is climatic.

That the trends under (1) would be similar is hardly surprising. The trends in individual regions are slightly different, which is quite compatible with (1). Given that the trends should be similar, I’m not overly impressed by the global trends being the same – there’s no guarantee that they would be the same with a different selection of stations; we don’t know whether PArker looked at other subsets and what tinkering that he did.

Also the trend estimates are estimates with fairly wide confidence intervals – and these intervals are probably under-estimates (based on consistent under-estimation by the Team elsewhere rther than replication of PArker’s estimates). Within the reported confidence intervals, the calm trend could be nearly twice the windy trend or vice versa.

I haven’t found the right source, but the percentage of the land mass covered with urban and suburban development now and over time would be very helpful. I don’t think it is a high percentage, but it is certainly growing.

I think that one school of thought is that the temperature measurements are taken at these developed locations more commonly than would be appropriate for a random sample of all of the landmass.

Other than for climate studies, who really cares what the weather is out in the uninhabitated sticks? The weather record from the 50’s is based upon locations where people cared about the weather. Parker is in effect arguing that this oversampling of the developed areas doesn’t matter.

It would be fine, if true, to say that x% of the landmass is developed, and is experiencing a temperature increase of y, while the undeveloped part is experiencing z. But no, we are asked to swallow the idea that we can measure the temperature in developed areas and apply that to the whole landmass.

We know the ocean is not developed, and it shows smaller changes. We know the upper atmosphere is undeveloped, and it shows smaller changes.

We also know that airports represent a very very very small percentage of the earth’s landmass and airports have experienced probably the most extensive use change of any other category of developed land. It is wrong to assume that a long temperature record at an airport provides a long period of homogenous temperature data. It is wrong to assume that airports are representative of the rest of the landmass.

Chris H, your back of the envelope computer is pretty good. The GAEZ study (a fascinating analysis, not to be missed) says that urban areas (including roads) are 0.23% of the total land area. Here’s the breakdown …

As Joe notes, the urban temperature is hardly representative of the land area … and the land area is only 30% of the planet.

To point out just a couple of things:
– oceans warming slower (or cooling slower) than lands on long-time trends is absolutely normal, because water is more difficult both to warm or to cool (I mean, we require both a bigger heat flow and more time); at the contrary, I see as a non-sense theory (made by some serrist, but don’t know who) that oceans are storing up heat, and that suddenly they will release such heat as a positive feedback: or the water warms than no heat can be considered ad “stored” (we have no phase change inside oceans, so no latent heat) or oceans begin to release heat but in the same time they have to cool (because they are losing heat); so, I don’t feel strange that in last years land temperatures for some series (NCDC and GISS) can be heating up while oceans are slightly cooling, but I feel strange that they are heating up so much to reverse global trend from slightly negative/stable to slightly positive; but, in the end, all this is not an evidence that lands’ warming is led by UHI (but, this effect, I would not exclude it from having a small part in temperature trends for some regional area, but just small); both because, as writtend, it is normal to have waters warming slower than lands, and because lands’ temperatures are often measured in a not so precise way (despite they continue to give us a global uncertainity in TT values which is barely the instrumental’s one) – but, to point out, HadCRU and MSU of last years (I mean always 2002-2006) follow much better waters’ temperatures trend;
– metropolis and larger cities temperature trends actually show an increase in UHI effect, but I think the sites are few, and the covered area is very small worldwide, so the global effect is very poor (but it still can be sensible for regional effects); but I would not run out a small warming trend for airport measurements due mainly to three things: increasing jet planes traffic, enlarging airports (then more buildings and more asphalt – if you follow motor sports, or simply live in a town/city, you will know how easy they get very warmer than air during day, and how much it can slow night-time cooling) and overall having airports nearer to cities (if not becoming an area inside the city after some decade of hurban growth, e.g. Milan-Linate);
– I found no point about UHI in towns and villages; you will tell me they are not large cities; but, in comparison with 20-40-60 years ago when they were “countryside”, many small towns and villages have become part of larger hurban areas (at least in Europe and Asia) so examining just larger cities would not be enough in my opinion to get a full view of UHI effect (still remembering that it has a small global effect: we can say many matters are due to UHI instead of GW, maybe even that a small part of measured GW is due to UHI, and that GW measurements are not so precise to make us able to make good analisyses and predictions, but not that GW is due to UHI).

Except that I think we should never just “throw data away”: data storage is practically free. And if one thinks that the data analysis is incorrect, the best solution is not suppression, but better data analysis.

#139: rightly, this is the matter of ground stations: even without sensible UHI effect, we measure temperatures in very small and concentrated areas – where not only UHI, but also soil usage matters – but that are not representative neither of the main part of land surfaces. As written above, it is natural that lands warm (cool) faster than seas: but, I think, it would be much better for studying GW (there are many other issues for lands even without GW) to point on waters rather than on inhabited lands (with a simple condition: all waters; not saying that Arctic pack is decreasing, making silence on Antarctic pack because it instead doesn’t fit AGW theories).

“The commonest explanation for it I’ve heard is from use of energy, for example, for heating. However, I can imagine that a city can grow without necessarily becoming more dense. Maybe you get a 1-degree heat island that grows from one mile in radius to 10 miles in radius. Will the weather station at the center pick that up? No.”

Consider that every site has the potential to become a hot box.

Well, Johnsen identifies several causes for the hot box.

1 Canyon effects. the floor of the box cant see the full sky
2.Multipath: IR bounces around inside the box.

At some point in development I suppose the geomtry effects tail off.
3. Wall effects: the walls of the box ( buildings) retain heat and produde heat
You could see this max out as well.
4. Waste heat: humans living around the box creating adifferentail that heats the walls
of the box.
I think your density question apply here. probably tails off at some point.
5. The box of hot air can’t be mixed easily. this effect would tail off
6. A Urban greenhouse gas lid on the box.

At some point, once you have a perfect hot box the only thing that can really increase
the UHI further is a better lid, a lid that traps the IR more effectly.

So theoretically, I suppose, ( i realize this is a toy model ) as Urbanization
encroaches on the site ( walls get built, asphalt floor) one would see
increasing trends in UHI, and as density went up, you see increases, to a point,
Then then the only thing left that can increase are the lid effects

General comments on the above – are people quite sure that Parker’s sites are mostly airports or is this a supposition because someone found that a particular subset of them?

Could it be that through choice of station and use of Tmin that Parker has completely missed all measures of UHI? I could believe that. But we could then say that Parker’s choice of stations and use of Tmin are a good choice for calculating an uncontaminated global temperature anomoly. What trends do Parker’s stations show? Warming trends!

Is it really feasible that they’ve all experienced the same increase in UHI as Steve McIntyre suggests in 50 years? I doubt it. However, it’s been amusing to watch the mutation of the UHI theory to the Suburban Heat Island Theory 🙂

My purpose in selecting sites. I knew the open feild would have no
geometry effects, no wall effects, little waste heat, no substantial shelter.
Only a GHG lid. temps ( min max mean) were flat for the last 24 years.
I also picked the feild because I knew the station there had everything
( wind, soil temps, insolation, rel humidty, precipitation,wind direction)

I picked the small nearby town close by to see Any kind of UHI differential.
my bet is the site is well situated, perhaps only seeing waste heat. I’m
not sure if Anthony has photos of the site, but I’ll check.

I picked the bigger town, 20 miles away, because it has seen rapid development
and we have pictures of the site and it is a mess. Located by buildings,
near asphalt, lots of windbreaks.

The point was to see the differential in UHI and to see the growth in UHI. Both
of which showed up in DElta temps and increasing Trends in Tmin.

I also picked the bigger city because its used by GISS as a quailty site.

One of the points of Anthony’s site survey is to document sites that have low
quality. Hopefully, the quality issues would be addressed. In fact NOAAs CRN
program seeks to establish a new high quality network of fully documented sites.
This network would provide an interest quality check on the existing network.

Finally, I guess I did the toy study to show how I think a proper study would
investigate the sites before dumping data in the hopper. Some ( over at RC)
has pointed out that you only need 60 good sites to characterize the global
temp. I’d ask the opposite question. How many bad ones do you need to contaminate
the record.

Parker’s American stations are all airports. Parker appears to have used raw GSN data. For example, the best that I can tell, he uses unadjusted data from Phoenix Sky. Even Hansen has a big urban adjustment for Phoenix Sky.

The UHI phenomenon is well-attested in boundary layer meteorology. Parker hypothesizes that airports are mixing rural air on windy days; I’m merely observing that airports are on urban fringes and are being folded into the urban area in many cities. That seems unarguable to me.

Another point that I didn’t make previously: PArker uses NCEP gridcell wind information as a proxy for wind at the specific airport. If you have a low correlation between NCEP gridcell wind information and airport wind conditions, then the trends are going to get closer and closer even if there is a real effect.

Phoenix Sky is one of the US sites in Parker’s list (all the US sites appear to be airports. I haven’t checked other places yet but the updated info in GSN/GHCN appears to be almost entirely airports. Phoenix Sky is not in USHCN. Here’s a plot. The GSN data coincides with the GHCN raw and GISS raw. Even Hansen adjusted UHI at Phoenix Sky (Whether his adjustment for the 1930s is adequate is different issue.) There’s obviously nonclimatic heating in the Phoenix Sky record. So there’s some UHI in the PArker network. To tell how much, you’d have to do quality control on the entire network.

As you note this data may be uncorrelated with airport winds.
I think the problem is larger than that.

Parker used what I think is an arbitrary method to chracterize
calm versus windy. The point of the wind is to MIX. Per Johnsen
UHI exists, in part, because areas dont allow mixing because of shelter. Now,
Mixing happens in turbulant flow. The wind velocity required
for this varies with the surface.
The question is, at what Wind velocity
does the flow over this surface become turbulant and destroy the boundary layer.
I think it depends on the site or the surface.

SO the question is… Was it windy enough at the site to induced
mixing.

Let me put this yet another way. With a Hot surface and a thick boundary
layer you might not get mixing until the wind velocity reached very high
speeds. 8m/sec might be “calm”. Parker’s sense of calm is human. The surface
and boundary layer care nothing for this. The question is not what was the
wind velocity. The question is ” did mixing occur”

So, change Parkers definition of Windy/Calm. See what happens to TMIN trend lines.

SteveM, this top post approaches self-parody, nearly to JohnA’s level.

Your top post discusses whether Parker has shown that there is no UHI, with suggestions tht he would be better off doing transects to measure UHI directly, and asking whether Parker has shown that UHI is an “urban myth.” But that is not the question Parker asks, nor is it what he claims to have demonstrated. You know what Parker actually claims – you show that you know quite well in your comments in this thread. In other words, your article directly misrepresents the Parker paper, and you re too intelligent not to know it – and I believe you owe an apology and an addendum to your top post correcting your misrepresentation.

Parker asks whether the change in average temperature over time in areas with UHI is different than in areas outside the UHI. He does this by looking at whether windy days, with outside air mixed into the urban environment, shows a different change than calm days. Parker’s experiment directly accepts the reality of UHI, contrary to your claims in the top post – Parker is looking at comparative rates of change over time, not at whether UHI exists. This is clear and obvious – you know it, you refer to this in your comments. Your attack on Parker in the top post is deeply dishonest.

I could go on and on – a potential issue with a subset of sampling sites does not alter the clear fact. There was a time, Steve, when I disagreed with and challenged much of what you said here, but paid attention and learned from you. When did you decide to descend to this kind of crap?

My impatient with this thread can probably be attributed at least in part to the grumpiness of a grumpy old man, but I think we have gotten sidetracked from the important issues that Steve M raised in his introduction to this thread.

The conversation has been civil and the information presented, in some detail, would be helpful mainly to those who have not read the Parker 2006 article. The trend lines for max and min temperatures on windy and calm days at a number of selected (how?) weather stations are nearly self explanatory in regards to what the author is attempting to show:

1. The trend lines for min temperatures on windy and calm time periods for 290 stations over the time period from 1950-2000, while intersecting the y axis at significantly different points (the windy trend lines always showing higher temperatures but the amount can depend on season), both trend at a 0.20 degree C per decade rate.

2. The trend lines for max temperatures on windy and calm time periods have essentially the same slope but intersect the y axis at different values with the windy trend lines on average intersected at higher temperatures but this relationship can flip depending on season of the year and region of the globe.
3. Parker indicates that 13 stations (of 290) show significantly different min temperature windy versus calm trend lines over the fifty year period that could be interpreted as indicating a presents of an UHI effect.

These are all interesting observations by themselves and deserve in my estimation at lot more discussion and background than was given by the author. Instead we have the Johnson et al. 1991 reference, given more or less in passing by Parker, being used evidently as primary bases for using these trends line differences as a proxy for an UHI effect. What I was hoping was that the detailed account in Steve M’s lead in to this thread of the Johnson reference would direct the discussion towards what is the basis for this major assumption made by Parker. Without showing more evidence that his assumption holds his conclusion becomes rather empty.

Perhaps, the evidence lies in the 13 evidently unique stations in the study or in a closer look at differences in temperatures between calm and windy time periods, but on these issues the author is relatively muted.

Like what the hockey stick of Mann’s did for natural temperature variations versus the Modern Warming Period, one can hardly conceive a better method than Parker’s for tying up the UHI effect over the period of most interest to the AGW advocates and doing it globally. Now if he can show good evidence for using the min temperature differences for windy and calm conditions as a proxy for the UHI effect and how he selected his weather stations, he might sway some skeptical minds.

#153. Personally I think that temperatures are warmer now than they were in the 1880s and would expect glaciers to retreat. I believe that this temperature increase exists independently of UHI effects. What I’m trying to understand right now is whether UHI effects have been entangled in temperature records and, if so, to what extent.

The “myth” being discussed here is not whether there is warming or not, but whether PArker has “demonstrated” that the temperature records are uncontaminated, as asserted by IPCC and others.

You know what Steve M actually claims – you show that you know quite well in your comments in this thread. In other words, your post directly misrepresents the Steve M’s claims, and I think you’re too intelligent not to know it – and I believe you owe an apology and an addendum to your post correcting your misrepresentation.

Now develop the greenfield into an urban environment so that UHI > 0. For some time during this development trend[UHI] must also be greater than zero. Apparently Parker did not detect an environment in this stage of growth. Perhaps airports exhibit UHI as a step change the day the concrete is placed.

I can’t say I’ve looked at this thread closely, but it seems there is generally agreement amongst the folks discussing here that UHI is a fact, but Parker’s position is that it hasn’t increased in 50 years in urban environments. Let’s suppose this position is correct, but when creating the global average I cut the percentage of rural stations in the measurement network from 75% to 25% (sorry don’t know the real numbers). I have in effect increased the UHI effect in the network regardless of the changes in particular urban environments

If a city is growing, but not growing in density, there will still an increase in temperature at the center of the city. For two reasons. First, any time there is wind, the wind will blow over more urban landscape before reaching the sensor.
Even when there is no wind, warmer urban air will radiate to colder rural air. The further away that cold rural air is, the less radiation will occur.

Finally, the entire scenario is extremely silly, since an urban area that did not increase in density as the population in the general area has increased, has never existed.

Hi Lee. Thanks for hanging out. Now, since the start of this we have had, I’d say, Half a dozen
interpretations of what parker said. Apportion the causation for the lack of communication as you see fit.

a good number of us have asked, in honest desparation, for a parsing of parker.

Neil King, has done a stand up job. Personally I think we reached an interesting and fruitful
Disagreement. He made me think. Same with milesworthy and others.. I don’t mean to single
Neil out, but he has been a badger of sorts, dedicated to understanding. Kudos to him.

So, Lee, when you write:

“Parker asks whether the change in average temperature over time in areas with UHI is different than in areas outside the UHI. He does this by looking at whether windy days, with outside air mixed into the urban environment, shows a different change than calm days. Parker’s experiment directly accepts the reality of UHI, contrary to your claims in the top post – Parker is looking at comparative rates of change over time, not at whether UHI exists. This is clear and obvious – you know it, you refer to this in your comments. Your attack on Parker in the top post is deeply dishonest.”

I’m a bit confused. I
1. Where ( quote the text) does parker ask whether the change in TMEAN over time in areas with UHI is different
than areas without UHI?

2. Where does he demostrate “outside air” mixing into an urban enviroment. I’m looking at the fresno
airport ( a site used by Parker) and I’m wondering which DIRECTION of wind Parker
used to specify that the wind was from “outside” a UHI area.. . Which direction
of wind brings “OUTSIDE AIR” to urban fresno? open question. I’m looking for that in parker.
Would it be wind from the East? or southeast.. I’m searching his paper and data and I cannot
find anything about “wind from outside the UHI area” In one sense all wind is from outside
of the site ( a point), but When santa Anas blow from the desert toward the southern california
coast thats a wind from outside the UHI area. ( hot as bloody hell)

3. Where does Parker classify sites as to whether they are UHI or not? How does he do this?
I missed the site surveys. UHI is caused by more than Population . It’s related to SITING. This
is why NOAA is redoing the network. Have a look at CRN. I can make a site in the middle
of wilderness into an HOTBOX. ( have a look at lake spaulding) So where are Parkers site
surveys?
Without a site survey showing the geometry of the site and composition of the surrounding area
This UHI classification is under question.

4. He did not look at WINDY days with outside air MIXED. He looked at wind VELOCITY
at the grid level. And he looked at the correlation of wind VELOCITY at the grid with wind
velocity at the site. Simple question. If I told you that the wind was moving 600 Miles per
hour over a surface, would you say that was a MIXING event?

Johnsen talks about Turbulance decreasng UHI, I think Parker needs to condiser
that the distinction is not CALM versus WINDY, the issue is Turbulant versus Laminar/stagnant. A hot
laminar wind will not cool stuff off. More simply. wind speed says little about MIXING. So, parker measured
velocity. He did not measure mixing at the site. I would have fewer issues with Parker if his
bifuraction were Turbulant ( mixed) verfsus laminar/stagnant. (unmixed).

I was sloppy – windy conditions reduce UHI in several ways, by mixing, by moving outside air in, etc. If you want to be pedantic, go ahead. It doesnt change my point. Parker very clearly stated in several places that he was looking at whether UHI caused significant differences in the temperature increase over time. He very clearly stated that UHI exists – see for example the penultimate paragraph of the Conclusions. When SteveM makes his clearly stated, several times, argument that Parker was disputing the existence of UHIs (what else is SteveM saying, when he argues taht Parker’s paper doesnt disprove the existence of UHIs) he was simply being dishonest.

SteveM argues as if Parker was trying to show that there is no UHI – that is simply false, and Parker very clearly states that he is not disputing that there is a UHI.

SteveM made a big deal about the Tmin argument, without mentioning that Parker also analyzes Tmax. SteveM takes Parker’s statement that the main impact of UHI (in context, clearly referring to potential impact on temperature trends over time) was likely to be on tMIN on cloudless calm nights, and tries to interpret that as if Parker is claiming that is the main conclusion of the Johnson paper. SteveM seems to think that ‘main impact'(of UHI) means the same thing as ‘main conclusion’ (of the paper). Johnson clearly states that the strongest UHI is indeed at night on cloudless windless conditions – Parker is simply stating the obvious assumption that if there is a UHI impact on trend, one would expect to see it when UHI effects are strongest, and Johnson does indeed say that cloudless windless nights are when UHI is strongest.

Good to see you comment back. Trust me I know the urge to shoot from the hip. So,
your apology is accepted. Anyway Let me address you point by point. I will keep
it civil, since my goal is simply to figure things out, hopefully make a contribution
maybe ask one good question.

So,

you wrote

“I was sloppy – windy conditions reduce UHI in several ways, by mixing, by moving outside air in, etc.”

Well, Windy conditions do NOT reduce UHI. TURBULANT conditions can reduce UHI. I’m just reading
Parkers source. Let me illustrate. STRONG WINDS FROM THE DESERT rush to southern california.
This is called the santa Ana winds. Its a blast furnace. Wind speed is IMMATERIAL. You get a nice blast furnace
called the desert sending its wind your way . Also, you are still being sloppy.
CALM STAGNANT AIR over the city: UHI effect.
LAMINAR flow through the city: UHI effect can be enhanced but not disrupted.
TURBULANT FLOW; ( wind in surges, eddies, chaotic flow, spkiy erratic velocity) Layer of hot air
canopy gets MIXED and heat in the boudary escapes to the sky.

All parker did was say “windy” means winds in the 3rd tercel. That is simply idiotic. The boundary
layer gives a whit about the decile or tercile of wind speed.

You go on:

“If you want to be pedantic, go ahead. It doesnt change my point.”

Well, call it what you like, I’d like a clear exposition of parker. NEIL and milesworthy have come the
closest. Personally, I have no issue with being pendantic. Badge of honor. Then again, I could just
say “wind blows the heat away” or “wind blows the light away” or “wind blows the IR radiation away”

“Parker very clearly stated in several places that he was looking at
whether UHI caused significant differences in the temperature increase over time.
He very clearly stated that UHI exists – see for example the penultimate paragraph of the Conclusions.”

I’ll have a look at that pragraph. Thanks. But King and Milesworthy already pointed out that Parker accepted UHI.
Their position is that Parker claims NO INCREASE in UHI over the last 50 years. So, My question to
Neil, explain why ,given the known causes of UHI, that ther has been no increase.

But you went further and said parker identified UHI sites. He did not.

You go on:

“When SteveM makes his clearly stated, several times, argument that Parker was disputing
the existence of UHIs (what else is SteveM saying, when he argues taht
Parker’s paper doesnt disprove the existence of UHIs) he was simply being dishonest.”

Well, actually, a bunch of folks thought the same thing. ( especially given the title)
After some time, some defenders have modified the hypothesis about what parker
claims, by saying, He doesnt deny UHI, only denies that UHI has INCREASED in the last 50 years.
Well parker also claims that urban weather stations are in PARKS. ( we havent found one like this yet
got any clues) Anyway, UHI increases, according to parker defenders stopped 50 years ago.
Why, given the known causes of UHI?

You conclude:

“I’ll also note, not for the first time,
that SteveM is making a habit of quoting partial sentences
without giving article and page numbers. Bad form – its a favorite technique of quote miners.”

here is the funy thing. When SteveM quotes something I can actually go check it out. I have the
document ( openness is good) I have a search engine. So I search for what he quotes. Then evalaute.
My sense is this. Most guys here would love to catch steveM on something. cause a good number of
of folks here get a kick out of finding mistakes. Pedants. I live for the friggin day, regardless.

The issue I have is with people who paraphrase. or the people who wave toward a source. or the people
who apply confidentality to their words.

But you went further and said parker identified UHI sites. He did not.

Parker lists 13 (out of 290 sites in his study) that show windy versus calm trend lines for min temperature that are in the direction he assumes would result from positive UHI effects. He says nothing about why he thinks these sites would show the effect nor even suggest more detailed study of them. I think he got the result he wanted and has now moved on.

Parker does not show the individual or collective results for these 13 sites either. He appears more interested in reporting combined trend lines for a period of 50 years than breaking down the results for individual sites or at least providing typical examples.

I cannot find a copy of this online, if someone is willing to send me a copy at willis_AT_taunovobay.com, it would be much appreciated.

More to the point, however … why is he using smoothed maxima and minima?

I see another problems with his analysis. This is that the NCEP/NCAR reports the wind at four times daily: 0, 6, 12, and 18 GMT. However, they do not report the average wind during the period, as they do with say precipitation. Instead, they report instantaneous values at those times. This increases the difficulty of determining what the wind might have been at an intermediate time.

To me, this question of the generality of using 4 x daily, gridded “winds” to compare to the local SAT is a glaring flaw in Parker’s argument. A line of thunderstorms comes through one city, mixing the air thoroughly, while another city fifty miles away is untouched … yet both of them are treated with the same “average” wind, whatever that might be.

In addition, the direction of the wind is critical. Wind blowing from the Poles is usually colder, while winds blowing from the Equator are warmer. Wind off the ocean is cooler than wind off the land. “Santa Ana” winds, or foehn winds, are hot winds … when one of them starts to blown, his assumption that wind will cool down the UHI gets blown out the window.

Wind is a local phenomenon, not a grid-scale phenomenon.

I repeat my request to those who argue that Parker accepts that there is a UHI. If so, given that

‘€¢ the global population has doubled since 1960, and

‘€¢ energy use has doubled since 1960, and

‘€¢ the urban population has increased a staggering 3.7 times since 1960,

what is the theoretical basis for Parker’s conclusion that there has been no change in the UHI?

w.

PS – I note a comment in Parker’s paper:

The analysis failed to detect the known urban warming at Fairbanks, Alaska (Magee et al. 1999), whereas the analysis using near-surface winds did so

If his method doesn’t detect the large and well-studied urban warming in Fairbanks, it is definitely a blunt instrument …

With regard to the wind, I assume that days in which it is windy at 0 and 6, but not windy in between are few. Even so, remember this is a proxy study. I assume there isn’t a source of more complete data for the full 50 years.

I repeat my request to those who argue that Parker accepts that there is a UHI. If so, given that…

I note that a notable increase in UHI effect has been detected in China in winter, but are there studies of UHI changes in places with less extreme increases in urbanisation? Should Parker have a theory about why UHI has not increased before adequate data on increases in urbanisation and likely effects has been gathered?

I assume that days in which it is windy at 0 and 6, but not windy in between are few.

This is not an intuitively obvious statement to me. To verify, I picked a random Weather Underground station, and viewed a week at the start of 2007, here.

In Colorado at least, wind can appear and disappear at any time of day or night. Estimating daily average wind speed based on four point measurements is going to be horribly inaccurate. Might as well choose random numbers.

#171 James
The wind would have to remove a statistically significant proportion of the UHI. Studies of Barrow indicate this does happen. Are there studies from elsewhere that show similar or different result?
#172 Hans
If there is no UHI trend in Tmin or Tmax, but there is a UHI trend in Tmean (or other measures of temperature), then the UHI signal should be detectable in the divergence between the two sets of figures. And Tmin and Tmax increases should be fair reflections of temperature anomalies.

A while back in the thread in discussion with Neil King, I did a little toy study.
( see #118)
I took the Marysville site ( which anthony has photo documented) and I compared it to
two sites in Colusa ( 20-25 miles away) From the photos of Marysville, we have a good
guess that the site is a hotbox. Buildings around leading to multipath Radiation,
and waste heat. Concrete and asphalt around impairing evapotranspiration, And shelter
effects from the wind breaks close by. Did you know a tree effects the flow stream
to a level 30 times its height? Anyway, I took the marysville site because it’s a siting
disaster and its used by GISS.

One site in Colusa was of particular interest to me. It’s an agriculuture weather station
part of californias pest managment system. Located in a feild.

When compared for the past 20 years or so, I saw this. Tmin in the feild was lower than
Tmin in marysville. No suprise, UHI. When I looked at the trend in Tmin, the difference
was widening. UHI getting worse. A local example of the global phenomena that Parker denies.

basically, with a direct method, with a method where you classify the site based on actual
survey, You don’t have to look at the wind. The increase in TMIN trend, which is the signal that
Parker is looking for under calm conditions, shows up under any condition.

So, now I’m in the processes of doing another little toy study. Parker Uses Fresno airport
as a site. AND Anthony is headed to fresno to document the
Site at the airport. Checking agriculature records on the area turns up a bunch of sites
within 30 miles. And one site a handfull of miles north at the university ( you wont find
this stuff in Jones or Hansen) The university looks a bit urban ( site survey will tell)
And there are other sites in the area that are clearly Rural, but for now, I tossed
The unversity data in the hopper.

In order to cut directly to the chase I have these questions for Steve Milesworthy and Neal J. King:

If the differences in trend lines for Tmin under windy versus calm conditions can serve as a proxy for a changing UHI effect how would this be calibrated?

If we cannot measure the UHI effect directly by some other means how would one calibrate Parker’s measurements?

Is not it the case that had Parker seen an even small but significant difference in his windy versus calm trendlines, he would have had a calibration problem in relating the trend line differences to a degree temperature UHI effect? Would not this indicate that a zero difference avoids all these issues?

Can either of you show in a post an excerpt from a reference that definitively and unequivocally spells out the basis for Parker’s assumptions for Tmin under windy versus calm conditions? And for that matter explains the differences for Tmin and Tmax that Parker sees for windy and calm conditions?

If we see a delta in Tmin, that would signal some UHI. If we see the delta GROWING over time, that signals increasing UHI. ( parkers arguement)

No, Parker’s method would be to split the dataset into windy vs calm.

As they say in some parts, there is more than one way to skin a cat. Parker’s is one method, Peterson’s (similar to what you’re proposing?) is another…that’s what science is all about.

#176 Ken said

If the differences in trend lines for Tmin under windy versus calm conditions can serve as a proxy for a changing UHI effect how would this be calibrated?

I would say that if you detected a diverging trend you would have to look for other data to calibrate it since the data Parker has used is not adequate for reasons given by others (ie. that UHI max and Tmin are at different times; that while wind measured 4 times a day may be a reasonable proxy for average wind it may not be good enough; that wind direction may be important).

If we cannot measure the UHI effect directly by some other means how would one calibrate Parker’s measurements?

Site-by-site analysis? Including assessment of increases of urban density and possible changes in local circulation patterns? More detailed mining of the data? Computer models?

Is not it the case that had Parker seen an even small but significant difference in his windy versus calm trendlines, he would have had a calibration problem in relating the trend line differences to a degree temperature UHI effect? Would not this indicate that a zero difference avoids all these issues?

Yes. He has detected 13 UHI-affected stations and a number for which data were confused by site changes or changes in circulation patterns. But he has not attempted a deep analysis of these stations. I would conclude that a null result for most sites is very convenient 🙂

I’m a little slow so bear with me. How do you arrive at the mean temperature of a site by adding the min and max and dividing by two? Wouldn’t a true mean require a time series? i.e. hourly temperatures added and divided by 24? Better would be slicing the time up into finer increments.

Wouldn’t there be a mean shift caused by day/night variations that does not show up in the (min + max)/2 number?

Steve Milesworthy, thanks for your thoughtful replies to my questions, but you may have overlooked my most cutting to the chase question which I have repeated below. To me it is major point of Steve’s M analysis of the Parker paper and one that on reviewing the posts here I do not seen a detailed reply.

Can either of you show in a post an excerpt from a reference that definitively and unequivocally spells out the basis for Parker’s assumptions for Tmin under windy versus calm conditions? And for that matter explains the differences for Tmin and Tmax that Parker sees for windy and calm conditions?

Two critically important things:
1) It is commonly assumed that GHGs as present in the atmosphere are well mixed gasses. In reality, they are not. Concentrations near sources are higher. Some of the largest sources are fixed sources. This in turn affects the realized radiational dynamics and therefore, the realized heat content. As a result, local anthropogenic thermal dissipation related effects on the realized surface temperature and boundary layer thermal profile are exacerbated.
2) Anthropogenic thermal dissipation related effects have been unfortunately oversimplied to the point of causing confusion by the term “urban heat island.” A better way to put it might be “anthropogenic thermal dissipation term.” Such a term is a multivariable function of location on the surface plus a number of other factors.

I’m just Musing. Supposition. The wind velocity at which the flow over the rural surface
becomes Turbulant ( a chaotic mixing feild) is lower than the wind velocity at which
the flow over the urban surafce becomes chaotic. ? true?

basically, the wind shelters of the “city” preserve dead zones, stagnation islands. and they also
create mixing zones.

{ Stupid question. has anybody put a city profile in a flow visualiztion system?
( water tunnel, or wind tunnel )

Hmm Money to be made. Installing Vortex generators on urban surfaces to increase mixing
and decrease heating and cooling requirements.}

More stupid questions. What happens down wind of the Wind farm in Altamont ( no bird kill jokes)

How quickly does the flow return to “natural” or normal.

Might be neat to look at the area ratio in an urban zone between dead calm areas and turbulant aress
as a function of wind volicity.. measured outside the city… Kinda like finding stall regions on a wing
hmmmm

If your station is in a stagnation island ( unmixed GHGs) chances are it is also in a IR wave guide.

I note that a notable increase in UHI effect has been detected in China in winter, but are there studies of UHI changes in places with less extreme increases in urbanisation?

which led me to look up the degree of urbanization in a few countries. Globally, since 1960 the world population has about doubled, but the urban population has gone up by 3.7 times. A couple of representative statistics:

World 3.7 times the urban population
Western Europe 1.4
US 1.8

So, while the US and Europe have not gained as much as China, the US urban population has nearly doubled.

UHI can also happen in small towns, as the example of Barrow, Alaska shows.

# 174 well done steve mosher. As steve Mc said in the original post this is obviously the right thing to do. There is no need to make a string of dubious hypotheses about wind, as done by Parker, or invent artificial equations, as done by Neal. The answer is right there in the data. Classify the sites as urban or rural (before looking at the data!) Then look at the trend in each group.
Here is another thing that is completely wrong in Parkers paper. He says UHI is most evident in summer. In fact the study by Easterling et al that he quotes seems to show much stronger UHI in winter than in summer.
There is another great misquotation here (#22). Parker: “Easterling found that UHI was little more than 0.05 degrees/century”. Easterling: “The difference in the trends is about 0.1 degrees per 100 years”. Well, I suppose it is true that 0.1 is little more than 0.05!

Sorry, but has ever anybody analised the impact of hurban air pollution on UHI?
E.g.: from ’40ies to ’80ies, “heavy” pollution covered the skies of all western cities, coal and oil burn, sulphates, lead etc. While, in the last 10-20 years, air pollution has much decreased and our skies are brighter.

I printed both papers without problems, on my Mac and on my PC (XP). I’m printing to an HP 4050N (Postscript) over ethernet from Acrobat 6.xx on both systems. Most problems I’ve seen, on other forums, printing Acrobat documents usually turns out to be a print driver problem. You might want to check to see if there is a later driver for your printer. BTW, I find it strange that your having problems printing to A4, when both papers appear to originate in A4 – I printed to US letter (8.5″ x 11″).

Parker printed fine here as well. The only difference I can see is that the Parker paper was originated in US letter format. If I print the A4 format papers (to US letter), without scaling, I see, as you reported, the top and bottom cut off. Have you checked the driver set-up in the control panel? Some times the Windows defaults over ride the the application print dialog. I have seen this A4/US letter mismatch reported before (on Windows, both directions) and generally it was a driver or print control panel set-up issue.

I’m wondering about the money to be made in doing EIR analysis for modifications
to the cityscape. Seriously, If these nitwits have their way, I’ll be paying taxes
out of my southern nozzle at a rate which will stall the intake.

I’m just Musing. Supposition. The wind velocity at which the flow over the rural surface
becomes Turbulant ( a chaotic mixing feild) is lower than the wind velocity at which
the flow over the urban surafce becomes chaotic. ? true?

Depends on the surface roughness. A flat plain is going to require a higher wind velocity for mixing than a built up area with a lot of single family housing. The corners of houses are especially good at inducing turbulence. They also increase the heat loss/gain depending on delta T. Round houses would save energy. They don’t seem popular.

Going back to the formal methods that Neal and others have used, I’d like to suggest one of my own, similar to Neals. First thing is I’m thinking that windy conditions are mostly associated with low pressure systems, and therfore cloudy and cooler. Therefore let
Trend[Ac] – Trend[Aw] = (1 – r)Trend[UHI] + Cw
where the new term Cw represents the cooling effect of windy weather systems. It is constant and trendless. Parker already showed that Tren[Ac] – Trend[Aw] is zero. This gives us the possibility that (1 – r) = 0 because Cw is trendless. Therefore UHI dissappears from the equation and Cw represents the difference between the 2 lines Ac and Aw. In other words Parkers results can be explained by the cooling effect of the windy weather conditions. Now, the obvious test of this claim is to do a control experiment with rural stations. If the Cw effect is correct then the same results should be obtained from the rural sites. However, if the rural sites do not show any difference between Ac and Aw, then the difference Parker has recorded is due to UHI which has no trend.
Parker has not performed this control so his conclusion is unproven.

Steve McIntyre: To begin with, I find references to “the Team” to be both cryptic and ad hominem. Wouldn’t it be more fruitful to stick to what’s on the plate?

Secondly, as Steve Milesworthy points out, to explain Parker’s null effect, you have to change from an Urban Heat Island (UHI) effect to a Suburban Heat Island (SuHI) effect, or rather to an Urban-embedded-in-Suburban Heat Island (UeiSuHI) effect. It’s all possible, but the explanatory value of this approach is being constrained because you have to add more (and more) special circumstances to have it apply.

Thirdly, a majority of the remaining explanatory value hangs on the supposition of a selection effect: the assumption that all or most of Parker’s sites are airports. This is the sort of thing that can be clarified with a polite inquiry. It seems that Parker has responded to such inquiries in the past with openness (another reason why remarks about his possible “tinkering” seem unwarranted). If the goal is to arrive at what can be fairly concluded by his analysis, it would seem that the best approach would be to boil the open issues down to a well-focused set of questions that Parker could be asked to clarify.

a) “Unadjusted data”: Why should Parker apply an urban adjustment? He’s depending on the urban heating to give him his signal.

b) “Correlation between NCEP grid-cell reanalysis and actual windspeed”: Parker studied that issue for 26 stations. For 18 out of the 26, the correlations were in the range 0.6 – 0.8. More detail is in Appendix C.

You state that what is at question is not the actuality of temperature increase (by which I assume you mean global average temperature), but the question of whether UHI effects have been entangled in the temperature records, and to what extent. And hence the question of whether Parker’s study constitutes a “myth”.

The main question that I see has been raised is one of a possible selection effect. This is a question that can be clarified; and should it be valid, it might be possible to re-do the study to take that overcome that obstacle. This would not be the first time that the first (or second) version of an experiment had to be refined in order to overcome concerns of that nature.

However, even if there is a selection effect, it is remarkable that the result of this effect is that the resulting trend in UHI is 0. To me this seems significant: I don’t see any particular reason why that number should be more characteristic for airports than any other number.

One could imagine scientific fraud; my point of view is that the game would hardly be worth the candle, as detection is too easy. Ultimately, Parker cannot control access to these data.

– When there is no wind, it would seem intuitive that the temperature would be higher in the center of the urban region than at the edge. However, if the direction of heat loss is vertically upward rather than horizontally outward, the difference would quickly saturate as the city grow wider. This is the sort of thing that has to be determined by a survey, certainly geographical and possibly over time.

– When there is a wind, the temperature of the breeze will depend not only on how long a distance it has passed through a city to get to the station, but how much heat it has picked up along the way. If the wind is more than a slow drift, it might not be very much at all. This is a question of some calculations, and possibly some experiments.

– The question of whether increased population leading to increased density does not seem silly to me: i) some aspects of the UHI do not depend on population density (Johnson); ii) there is a limit to how close people like to live to each other, sometimes enforced by housing codes.

#205. Let’s step back a little on this: if Parker is going to use NCEP gridcell calm-windy as a “proxy” for UHI, then I’d like to see at some examples where it is agreed that there is known UHI and the NCEP gridcell calm-windy distinction picks off the UHI. In two cases that I’ve looked at – Phoenix and Fresno – both of which GISS allocates UHI, the Tmin tends show nothing.

IF NCEP gridcell calm-windy draws were unrelated to Tmin, then you’d get identical trends. This seems like a plausible null hypothesis and I don’t think that it’s been ruled out.

No one’s suggested “Scientific fraud” here; that’s a complete red herring. These folks are trying to do fairly subtle statistical analyses and may not be very accomplished in this area.

While Parker may have somewhat oversold his results, I’m also critical of the lack of due diligence on the part of IPCC to see exactly how much weight can be placed on these results.

The question at which Parker’s study was addressed was the question: “Could the global warming apparent in the record of land-based temperatures be due to an increase over time in the local UHI effects?”

What this means is that the question of whether or not urban areas are representative of the world’s land area is completely separate from this question: That speaks instead to the question of whether an urban trend can be legitimately interpreted as a global trend.

But Parker is asking about the actual (as opposed to artefactual) nature of the weather-station trends themselves, not about whether they are representative.

Remember that Parker has published trends for 290 stations over 50 years from which he concludes, and I assume Neal King agrees, means we have no UHI effect “on average” over tose stations and time period. Using that conclusion, what if on looking at individual station data on an annual basis we find that some years the trend indicates a UHI effect and other years in shows an anti- UHI effect and perhaps other years no effect? Would this mean that the UHI effect changes with time? Or that other effects are operating? How would these short term effects and oscillations be explained? Are the UHI effects only seen on a long term basis? And why? Do not we need this information, that must be available from Parker’s study, before making any hasty conclusions based only on long term trends averaged over many sites?

– “Why is Parker using smoothed maxima and minima?”
Probably it was faster and easier. He doesn’t acknowledge anyone for help in doing data analysis, so he may not have had much help. He may have figured that if the results of the study looked interesting, surely someone would be willing to do it more thoroughly later.

– It’s easier to calculate average precipitation than average wind velocity: precipitation is collected over time, but measured at discrete times. But a velocity measurement only relates to a specific time.

– Your concerns about the nature of trying to analyze a continuous quantity like wind velocity by measurements discrete in time and space are reasonable. However, these issues are inevitable in that he is trying to wrest information from data that are both limited and irreplaceable: One cannot go back over the last 50 years and do a better job. And he has addressed the question in Appendix C: the correlations of wind in time & space afford some confidence that there is a meaning to his calculations. The very fact that there is a gap between the calm- and windy-day readings indicates that there is some reality that he is reaching, even if attenuated by less than 100% correlation.

Parker uses Tmax as well as Tmin to evidence UHI effects, as I believe I described in #15. There are some complications due to the near-surface temperature inversion stuff, but I would go farther than saying that the Tmax differences between calm and windy days is a proxy for UHI: I would say that it is a measurement of it.

There are limitations imposed by journals on how much space they are willing to give an article. In addition to that, they often impose page charges. For these reasons already, it is impractical for a scientist to publish all the data upon which his conclusions are based.

However, Parker seems to have been willing to make more information available upon request.

Neal (#205), it may be the case that UHI magnitude could be inferred from calm/windy contrast data (if so, kudos to Parker for devising such a clever approach). However, that inference would depend on a long chain of physical arguments whose every link would be subject to challenge. It is certainly not the direct way to estimate the effect.

One could imagine “designing” a direct, randomized, experiment: Paired sites (or, better, the same site with multiple treatments) and contrasts between “treatment” and “control” results. However, such experiments tend to be costly.

As Christy has shown, there is another way to proceed. Although one can always raise issues about observational data (these would apply to Parker, btw), a direct experiment can be conducted by pairing sites which (non-randomly) happen to be subject to treatment with sites that are unaffected. If I understand correctly, this is what Christy has done in California, and it would seem to be the obvious way to settle the question (ignoring Anthony Watts’s recent discoveries).

Under the circumstances, perhaps it is not unreasonable to wonder what benefit the Parker method offers.

Finally, use of “the Team” may be cryptic but it is not ad hominem. Rather, “the Team” is simply shorthand for a very interesting and surprisingly popular form of post-modern inquiry employing unique but nonetheless recognizable methods.

#205, 213. We’ve discussed the etymology of the term “Hockey Team” and its shortened version Team in the past. I didn’t originate the term – it was originally used at realclimate to say that I wasn’t merely challenging Mann; I was challenging an entire Hockey Team.

I prefer not to speculate about what I should conclude on the basis of specific details on the short-term behavior of data that I haven’t seen. There are too many possibilities, and I’m not close to experimental procedures of that type.

My general approach is to assume that the scientist involved is not deliberately trying to fool me. My next step would be to examine the paper (and interview the scientist, if possible and if it seemed sufficiently interesting) to see if certain problems that I could foresee had also been foreseen by the experimenter. If I start to worry either about the competence or the basic honesty of the experimenter, then my antennae go up. But if I get reasonably well-considered answers to reasonable questions, I’m going to give the guy the benefit of the doubt, unless the results are so unreasonable that thorough checking is needed.

It seems that other people have been able to get additional information from Parker on this analysis. So, one useful thing that could be achieved by this discussion would be to boil down to the essence the questions that leave some “wiggle room” in Parker’s conclusion, so that he can be asked to clarify them.

In Section 3, Parker points out that when he tried out a change of definition of “calm” from “lightest tercile” to “lightest decile”, the global trend in Tmin did not change. To quote him, “So the overall analysis appears not to show an urban warming signal and to be robust to the criterion for calm.”

1) Tmean is not used by Parker: It would also average the effect of the nocturnal temperature inversion. Likely to add confusion!

2) A correlation between temperature and wind direction would reduce the Ac-Aw and Bc-Bw signals. However, it seems to me that it would reduce the trends to about the same degree as it reduces the signals themselves, so if you’re seeing the signals (and you are) you should not feel too paranoid about being fooled about the trend.

3) Site survey: This sounds like a good follow-up project. His judgment on UHI vs no-UHI is based on the w-c signals. If someone else wants to follow on with an on-site study, the door is open. But based on Parker’s work, at least you know why you care…

4) Johnson et al.’s paper mentions wind, but in fact their method of analysis doesn’t actually deal with it. All of their calculational methods assume a nice still day. Personally, I don’t think the difference between turbulent and laminar flow is all that significant for this issue: In either case, you’re going to be mixing air of one temperature (“outside”) with air of another (“inside”).

– Personally, I would think that the proper division between “windy” and “calm” would be some specific speed. However, Parker chose to use percentiles. However, as mentioned above (somewhere), when he altered his definition from lowest-tercile to lowest-decile, there was no significant difference in the trends. So that suggests that his definition was not sufficiently bad to make any difference: windy is windy.

– It is a question as to why a growth in UHI hasn’t been more evident. I suspect that a detailed topographic study of a city over some time may be necessary to answer this question. My casual idea is that there will be some kind of saturation: the UHI doesn’t just get hotter and hotter as you get deeper into the city, but there is some kind of saturation. But that’s just an off-the-wall thought.

The point is that a direct method is not always available. Parker is trying to milk what he can of data that are actually available from the last 50 years. This stuff cannot be replaced. Any attempt to salvage it is worthwhile. Of course, you have to think about what you’re getting. Until we have a time-machine, there is no replacement for these data – but you can add to them.

With regards to “artificial equations”: mathematics is a way of abstracting information out of data; and of expressing relationships that are sometimes too clumsy to express in words. It was clear that there were aspects of Parker’s analysis that were not well-grasped by some participants in the discussion, but have become clearer. That was the point of the equations.

I think your judgment on Steve McIntyre’s original posting is a bit harsh. It is my impression that the understanding of Parker’s approach has shifted during the course of this discussion, and Steve McIntyre has participated in this shift.

Unless subsequent postings show a “backsliding” into a less sophisticated understanding, I don’t think your accusation is justified.

It’s likely that, if the weather station were not present for all 50 years, he would have omitted it from the study.

If a weather station is installed at an airport, that probably means that it was installed at the time of the creation of the airport, or shortly thereafter: As someone remarked above, who care’s about the weather in the middle of the sticks? But what that means is that it’s quite possible that no one ever installed a weather station and then found out later that someone built an airport there:
Either:
– The station was installed after the airport was built; or
– It has nothing to do with airports.

1) Over the few meters distance between the ground and the weather station, I doubt this is going to be significant. (Maybe Pielke’s articles will convince me otherwise – I haven’t had a chance to look at them.)

– First of all, you have inserted Cw, a cooling effect, in an equation about TRENDS. The only way this makes sense is if you are talking about the TREND of the cooling effect of the wind. But you have also stated that the Cw is trendless, so the TREND = 0. You should instead go back to Eqn.(8) in #15.

– Anyway, if (1-r) = 0, there would be no gap between Ac and Aw, and Bc and Bw.

– Actually, I discussed this already in #78.
I think you need to study the situation further.

Thanks for the reply, Neal. You have contributed well to one of the most thoughtful discussions I have seen here recently.

Anyway, it appears I wasn’t very clear in my first post. Then network I was refering to was not the stations used in the Parker study, but any one used to compute a US or North American average for instance.

As an example suppose in 1950 50% of the station used for computing a US temp average were rural, and 50% urban with constant 3C UHI effect (not increasing as per Parker). Over time fewer rural stations are available, and by 2000 only 25% are rural. Obviously the mean US temperature would increase due to the weighting of UHI effected sites (off top of head 0.75C), even if the UHI contribution at each particular site was constant.

– The benefit of Parker’s method is that it makes use of 50 years of data that have already been gathered, and which no one is ever going to be able to collect again.

– It does not complete with/exclude/prevent other experiments. It is what it is. Additional information is always welcome. But the basic data cannot be replaced. We are not going to be able to re-run the last 50 years, and we have no time-machine.

– To me, the implications of using a distancing term like “the Team” seem vaguely paranoid. And why, in a public blog which presumably invites public participation, are you using “gang language”?

My impression remains the same, as expressed in #235: Unless you want to address only a closed group of committed initiates, there is absolutely no point – or advantage – in objectifying people who don’t agree with you as a monolithic entity incapable of straightforward logical thinking. It doesn’t make your argument any more convincing.

re 227, Neal, you say:
“I think your judgment on Steve McIntyre’s original posting is a bit harsh. It is my impression that the understanding of Parker’s approach has shifted during the course of this discussion, and Steve McIntyre has participated in this shift.”
—

SteveM’s original article here is disputing whether Parker has shown that there is no UHI. That is a fundamental misstatement of what Parker is arguing, and Parker’s purpose is NOT subtle or easily subject to misreading. Even in just the 4-sentence abstract, sentence 1, the first 7 damn words of the entire article, begins by EXPLICITLY ACCEPTING THE EXISTENCE OF UHI as its premise, and ends by saying “global and regional TRENDS are compared.” Sentence 2 begins by saying “The TRENDS in temperature…” and ends with “… the observed warming TRENDS.” Sentence 3 begins with “The TRENDS of temperature…” includes in the middle “agreement with published TRENDS” and includes in its last clause “sampling of global TRENDS.” How one can read even that short abstract and miss that this paper accepts the existence of UHI, and that it is about the impact of UHI on the magnitude of temperature TRENDS over time, is utterly beyond me. And yet, SteveM did so – or at least wrote an article as if he did.

To make it even more explicit (and I’ll ignore all the intermediate points where he makes it explicit that he is talking about trend) Parker includes a paragraph near the end of his discussion, where he spells out that he is talking about trends, and that he is explicitly NOT disputing the existence of UHI. “The reality of urban warming on local and small regional scales is not questioed by this work; it is the impact of urban warming on estimates of global and large regional trends that is shown to be small.”

When I first read Parker 2006, a few months back, I got this immediately – it was obvious what the paper was, and wasn’t, about.

For SteveM to have written an article asking how Parker shows that there is no UHI, shows that either SteveM criticised a paper he hadn’t read well enough to have even a basic cursory understanding of what the paper is about, or that he is being dishonest about it. Giving him the benefit of doubt – a huge leap – and granting him emerging understanding during this thread, for him to leave the top article uncorrected is also dishonest, IMO. AS I said in my first post quite a way up n this thread, SteveM owes a corrective paragraph or two – leaving his original untouched so the comments make sense – and an apology.

I also find reading through Parker again, that SteveM **IS** engaging in quote mining – foolishly, from the very paper that this article is about. SteveM says:
—
Update: Parker observed in connection with a study by Peterson:

many “urban” observations are likely to be made in cool parks, to conform to standards for siting of stations.

Parker’s US stations are all at airports, which I would not be inclined to describe as “cool parks”.
—-

Unfortunately, that wasn’t Parker commenting “in relation to” Peterson – it was Parker relaying a possible explanation made by Peterson. Parker noted that Peterson found no impact of urbanization in trends between sites, when controlling for “elevation, latitude, time of observation, and instrumentation…” Parker went on to say “One possible reason for this finding was that many “urban” observations are likely to be made in cool parks, to conform to standards for siting of stations.” Note the “was that” – this was an explanation from Peterson. Note that SteveM truncated that first part of the sentence, n a way that left the sentence looking is if it WS Parker commenting. This implication is false – and is utterly irrelevant. Whether Parker’s stations are in “cool parks” or if there are other reasons for the lack of trends differences, does not change that he detected no or minimal trend differences. It is simply a potential hypothetical explication, made by someone else, and included in the literature background portion of the introduction, of previous work that had also found minimal impact of urbanization on temperature TRENDS.

– There haven’t been these radical urbanizations around existing weather stations; or

– the UHI saturates at some point, so that increasing city size doesn’t increase the peak UHI beyond a point; or

– Perhaps another reason.

I guess a better question than “Is an increasing UHI effect fooling the IPCC into believing in AGW?” would be, “Where is the increasing UHI effect?”

This is like the Sherlock Holmes story: A farmhand has been killed at night. Holmes investigates. A guard dog is found on the premises. Holmes muses about the curious behavior of the guard dog in the night. Watson interjects, “But the guard dog did nothing in the night! Not a bark.” Holmes: “Yes, that is indeed the curious behavior of the dog in the night. If a stranger killed the farmhand, why didn’t the dog bark?”

I don’t know. But I think more progress will be made through sincere and open-minded inquiry than through suspicion.

#239. Neal, I asked this generally, but it got lost. Can you give me any citation containing measurements in any city confirming in an urbanizing situation that NCEP gridcell windy versus calm trends are a indicator for urbanization (in a known environment). Without some ground truthing, it’s just a “proxy”. So are tree rings.

In the one site that I’ve examined data (Freso in another post), there is a known urbanizing setting and windy-calm doesn’t identify it.

You spend the first two paragraphs of your damn article talking about the existence of UHI. You argue that one coudl ealsily see of UHI exists by taking a transect. You INTRODUCE THE DAMN ARTICLE by talking about whether UHI exists.

You then tie Parker into that introduction, with your ‘theological literature’ absurdity.

You are WAY too skilled a writer for that not to ahve been intentional.
—
You go on to say:
“But the real question in all of this is why would one use a “proxy” like trends between windy-calm nights (and there’s lots of hair on the wind-calm information which comes from NCEP re-analysis on a gridcell basis only) to analyze UHI when temperatures can be measured directly across a transect. Here’s a figure from Oke et al showing UHI measurements in a variety of urban settings.”

That point is IRRELEVANT to Parkers trend analysis – it is relevant to whether UHI exists. A transect would tell us nothing about whether there are urban impacts on large scale or global TRENDS. It would tell us whether a UHI exists – and we know that UHI exists. If you weren’t intending to be discussing how Parker is relevant to the existence of UHI, then why did you include it? It isn’t a relevant question here – because Parker isn’t measuring the existence of UHI – he accepts it – he is measuring whether it has an impact on temp trends over time.
—
You conclude (well, except for your quote mine) with:
“Has Parker shown that the belief that UHI affects CRU and GISS averages is an urban myth? Or is the belief that Parker has shown the irrelevance of UHI to large-scale averages an urban myth?”

Parker isn’t addressing whether UHI affects the AVERAGE temperature. He accepts UHI, explicitly – of course that will affect the average temperature of gridcells that have urban stations. He is addressing whether UHI affects trends in averages over time.
—

If you weren’t intending to muddy the water over whether Parker is about the existence of UHI, then why did you spend so much time in this article discussing possible experiments, and data sets, and existing papers, and averages, that address whether there is a UHI, and that do NOT address Parker’s point about the impact of UHI on trends over time? That is utterly irrelevant to Parker.

Lee, you’re getting overwrought because you don’t understand the role of anomalies in the large-scale averages. You say:

He accepts UHI, explicitly – of course that will affect the average temperature of gridcells that have urban stations.

That’s not correct. IF you were averaging actual temperature measurements, this would be true, but the gridcell methods convert temperatures to anomalies. If there’s no change in UHI effect, then the any UHI is washed out in the anomaly. I repeat one more time: because CRU and GISS are anomaly based, having an impact on the large scale averages can be accomplished only through trends and vice versa. I assumed that everyone understood that, but obviously you don’t. There’s nothing incorrect in my characterization of Parker; it’s just that you don’t understand how anomaly systems relate to trends and vice versa. I might have expressed it more clearly for people that aren’t up to speed, but that’s a very different matter.

I discuss and describe UHI to introduce things. I provided a companion post Godowitch et al that discussed UHI in more detail.

Anomalies simply take the average of the observed temperatures (daily, monthly, annual, max , min, or what have you), and convert them to a scale with a different zero point – a zero defined as the mean observed temperature over some accepted calibration period.

If the mean annual temp increases from 5C to 10C over a 100 year period, and the anomaly method converts that to an increase from -3C to 2C, then we get precisely the same increase of 5C/century. If that mean includes an urban site that is consistently 2C hotter than the rural sites, but increases at the same rate, then the converted anomaly increase will also show an increase at the same rate.

Teh gridcell “average” for agiven date,or the now baseline ‘average” zero point, does not affect the trend. A UHI will raise the average temp of the gridcell for all time points, but will affect the trend ONLY if it is either warming or cooling – and it is the warming or cooling TREND that Parker (and Peterson) examines, not the average.

Not to mention that you reference to taking a transect is ONLY relevant to measuring whether an UHI is present, and is irrelevant to trends. You are being disingenious, Steve.

I will grant, Steve, that one can make a (very) sideways argument that by using ‘average” you were referring to the anomaly trend. That usage is in line with the rest of your article, though – you are a very good writer, and I believe yo write for exactly the impact yo want – and doesnt address the transect stuff, nor the irrelevant to Parker data showing a UHI (not a trend), nor the quote mine.

Parker acknowledges that there is a strong Tmin warming trend, windy vs calm, across europe and asia on winter nights, and that this trend is concealed in the global annual average. He understates this. The windy warming trend is strongest in europe and asia on windy winter nights, but it holds across his reported data (Table 1), for all seasons.

What I would like to know is, where on earth (literally) is the opposing cooling trend that accomplishes the concealment?

It is not in the Arctic, or Europe, or North America, or Asia. It is not in the entire Northern Hemisphere north of 20*N, nor in the Tropics north of 20*S. And it is not in Australasia. So says Parker.

That only leaves the very southern tip of Africa, the skinny part of South America, and Antarctica. WTF?

Parker asserts that UHI effects on the land surface temp record would be shown by a difference in trend between windy and calm nights. He finds such a trend up in the 95% of the planet’s landmass that hosts 99% of the worlds people.

And given that he is able to make his asserted case by zeroing out that warming trend for the global, he must have found a really strong trend in opposite direction, somewhere in the rest of the planet that has no people.

It appears to me that he has either demonstrated an effect of UHI on the temp record, or invalidated his assumptions and/or methods.

I appreciate your admonition that “using a ‘distancing term'” like “the Team” may seem “vaguely paranoid” [“gang language”], and I agree that snarky remarks tend to discourage serious engagement. Your point is well taken.

With respect to the Parker paper, I am still withholding judgment. Clearly Parker (2006) has devised a clever way to address the UHI issue. However, each time I look at Parker I find myself with more questions: How were sites selected? Does the sampled population represent the target population (clearly no; but does the difference matter?). Does the conclusion depend upon averaging across multiple populations, some exhibiting substantial UHI effects and others exhibiting offsetting trends (clearly it does, but how important is this?). Why are all the significant trend differences in the wrong direction (“Urban Cool Island”)? What is to be made of the reported uncertainties — on the order of 0.1 degrees C/decade for the trends in Tmin and Tmax — which are substantially larger than the signal we’re trying to detect (given sufficient background noise, even a hound’s barking can go unnoticed).

Also, changes to sampling protocols over the period of record raises a host of other concerns.

Nonetheless, I think we agree that this is interesting, and I share you modest assessment that the

benefit of Parker’s method is that it makes use of 50 years of data that have already been gathered, and which no one is ever going to be able to collect again.

But I would still like to see some direct evidence. For me, Parker’s result remains a paradox, not an explanation.

Parker acknowledges that there is a strong Tmin warming trend, windy vs calm, across europe and asia on winter nights, and that this trend is concealed in the global annual average. He understates this. The windy warming trend is strongest in europe and asia on windy winter nights, but it holds across his reported data (Table 1), for all seasons.
What I would like to know is, where on earth (literally) is the opposing cooling trend that accomplishes the concealment?

Good question. Here’s a barplot showing distinct regions from Table 1 (Annual), excluding NH north of 20 which is a subtotal. For 241 out of 265 stations, there is regionally a greater trend on windy days than calm days. On Parker’s unproven hypothesis that this trend is an index of urbanization increases, this would indicate a de-urbanization in most of the globe during the past 50 years.

The offset as you note is in the Tropics, but there’s something wonky here as the trends are only 0.17 versus 0.18 over 24 stations: located in Thailand, Phillipines, Malaysia, Colombia, U.S. and French colonies. There doesn’t seem to be enough weight to offset the other stations. Very strange. There are some other wonky entries in Table 1- some trends for “all day” exceed both the calm and windy trends, which is impossible. So there’s something wrong here.

I dont know that there is anything wrong here. Clearly, Capetown and Buenos Aires must have ENORMOUS UHIs. 🙂

Of course, this does raise the question as to why the >20*S Southern Hemisphere (apart from the relevant portions of Australasia) is conspicuously missing from Parker’s report. He shows stations in that area (Fig 1), and claims they were used in his grid analysis for the global, but does not report the regional measures and trends. I wonder. Why, why, why, why, why.

– The benefit of Parker’s method is that it makes use of 50 years of data that have already been gathered, and which no one is ever going to be able to collect again.

Actually, he’s comparing 50 years of temperature data to 50 years of computer generated winds … but if that is valid, why not compare 50 years of computer generated winds to 50 years of computer generated temperature data?

I don’t understand why you are suggesting that Parker is assuming windy-calm to be a proxy of urbanization. What he seems to be doing is using that quantity to detect a boundary-layer issue in temperature. In principle, that could be caused by UHI, a burning trash barrel, whatever. He’s not primarily interested in urbanization as such. However, if there is a UHI effect, it’s natural to expect it to show up.

Actually, if you look at the graphs, it’s usually warmer on windy nights. For example, in the caption to Fig. 6: “Tmax is, as expected, lower on windy than on calm days in summer (b), but higher on windy than on calm days in winter (a), because persistent near-surface inversions are limited to calm weather.”

The reason is that on calm nights, the still air protects the temperature inversion (from the rapid cooling of the ground due to IR radiation to the sky), whereas on windy nights the inversion layer is disturbed.

I’m still waiting for someone / anyone to explain how the population can double in the world without increasing the UHI, if it exists, (which all, including Parker agree is the case.)

If it exists and isn’t found by Parker’s method then either the physical explanation must be given or the flaw in Parker’s method must be found. He can’t get away with simply saying that UHI exists but has no effect worth mentioning.

1) The NCEP/NCAR Reanalysis project is probably much easier to work with to get comparisons over a 50-year period. Stealing a quote from the site
(http://www.cpc.ncep.noaa.gov/products/wesley/reanalysis.html#intro)
“Until recently, the meteorological community has had to use analyses that supported the real-time weather forecasting. These analyses are very inhomogeneous in time as there have been big improvements in the data assimilation systems. This played havoc with climate monitoring as these improvements were often produced changes in the apparent ‘climate’. Even fundamental quantities such as the strength of the Hadley cell has changed over the years as a result of the changes in the data assimilation systems.”

2) Parker does address this issue in Appendix C. He believes that it should not cause a global bias exceeding 10% of the observed warming signal.

The way I read it, what he’s assuming is that there is a part of the temperature measurement that can be blown away, and calm-windy helps him do that.

If there has been a UHI signal inflating the GW trend, then his point is that it should show up there. But if the sites are not urbanized, then UHI cannot have been inflating their temperature rises anyway, right?

So it’s a matter of crossing the river twice: I don’t believe Parker’s point depends on whether the sites are urbanized, but whether or not they’re representative of the globe – a point we have been stumbling across.

Neal J. King, while your attempts to clarify what Parker has explained/imparted in his paper have been admirable, if for nothing more than the shear effort of it all, I think most of us, in the end, however, including you — I think — can agree that for Parker’s paper being considered an authoritative source and on a critical question vis a vis climate policy, it leaves too many questions unanswered. I find no conspiracies here, but I am puzzled why these questions have not been settled by the climate community or at least more concerted attempts at settling it than I see being made. His “averaged” global finding is a very convenient one for the advocates of AGW, much in the same manner as Mann’s HS was.

I think he owes some answers before the more skeptical amongst us will be satisfied. I only hope his reaction to questioning is better than that Mann has shown. Without the answers from Parker, I only see digging into the available data for contradiction and confirmation as being productive ‘€” and that is where I think Steve M does a particularly good job.

2) Parker does address this issue in Appendix C. He believes that it should not cause a global bias exceeding 10% of the observed warming signal.

He believes it…hmmm…odd wording, isn’t it? especially for a scientist? Does parker prove it, or offer supporting evidence? or does he just hand-wave his belief? ( I don’t have appendix C, if you have it, send it along, i’d like to see for myself.

One thing that I failed to do in setting up this post was to quote the relevant IPCC comment, which was on my mind, but not necessarily clear to others. IPCC AR4 stated:

In a worldwide set of about 270 stations, Parker (2004, 2006) noted that warming trends in night minimum temperatures over the period 1950 to 2000 were not enhanced on calm nights, which would be the time most likely to be affected by urban warming. Thus, the global land warming trend discussed is very unlikely to be influenced significantly by increasing urbanisation (Parker, 2006).

While Parker’s article seems flawed to me, it’s an only an academic article and that’s the price of eggs. But how can IPCC opine, based on this very speculative proxy, that it is “very unlikely” (And remember that IPCC defines “very unlikely” ) that there has been a UHI impact. Possibly PArker’s article provides evidence (although I don’t grnt that). But in an international scientific commission, this article does not rise to “very likely”.

Note that PArker was a chapter 3 lead author like Mann was for the TAR hockey stick. While one can quite reasonably lay off being overly critical of PArker as author of PArker 2006, he has different obligations as an IPCC author and one might well choose to be critical of PArker in hio his capacity of IPCC lead author approving the above claim.

However, I also think that it would be most fair if some effort were made to create a finite list of well-defined questions that would be likely to satisfy the questioners. I don’t think anyone looks forward to being hounded by an endless stream of “why”s.

If we lay out a polite but thorough list of non-redundant questions that address the study that he actually did (and are not disguised forms of “Why didn’t you do a different study?”), I can only imagine that he would be pleased to answer the questions. (Whether he would have the time or energy to re-do the analysis on different lines is another question.)

In Appendix C, Parker compares measured wind speeds at 26 locations in NA and in Siberia with the corresponding NCEP-NCAR reanalysis values. 18 out of the 26 correlate in the range 0.6 – 0.8. In general, the ratio of actuals to reanalysis values dropped when the satellite data were added into the reanalysis mix, but by less in NA. He goes on to conclude that the small fractional change in station wind-speed values relative to reanalysis values are unlikely to have caused a global bias in trends of Tmin in calm conditions approaching -0.1-C over the 50-year period, equivalent to -0.02-C/decade, 1/10-th of the observed warming signal.

I agree there is a question. The fact that Parker comes to this conclusion, however, may not be a problem for Parker: It could be a problem somewhere else.

Actually, I was putting together a list of questions intended for him, and this was one. However, when trying to formulate a brief synopsis of Steve McIntyre’s idea, I might have found a hole in it. So I decided to stop and think it over some more.

If we lay out a polite but thorough list of non-redundant questions that address the study that he actually did (and are not disguised forms of “Why didn’t you do a different study?”), I can only imagine that he would be pleased to answer the questions. (Whether he would have the time or energy to re-do the analysis on different lines is another question.)

Obviously the straight forward requests would be to obtain sufficient information and data to repeat his analyses and to do the analyses differently, and, for my preferences, look at the data in greater detail with regards to individual sites and shorter time periods. Those are requests that really would only formalize what he should already have provided or be prepared to provide as a practicing scientist. It should require little extra effort on his part.

Other questions for Parker would and should be, in my mind, limited to asking him for the details of his (hopefully a prior) site selection process and rationale and more details on the scientic foundations of using such an indirect method as his trend comparison when one might intuitively think more direct ones are available. But those are my views and perhaps why I think a query by committee may not be particularly effective.

Taking my own suggestion, here is my first draft of a set of questions intended to capture the issues that seem to be left open by Parker’s paper:

“- We have noticed that, of the 290 stations included in the analysis, the U.S. sites are all at airports. Are all the 290 sites at airports? Can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

– [The following needs more work, but I’m going to sleep on it]: The following interpretation has been suggested: There is an increasing UHI trend at airport sites, but the airports are surrounded by suburban areas that are also experiencing increasing UHI. On windy days, the admixture of air from the suburban area reduces the windy-day temperature (so there is a calm-day – windy-day signal)…..

– There is some question as to how the global trend of UHI averages to zero, although the trends of nearly all regions for which trends are reported are positive. Can you provide some insight on that?

– Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

– You have suggested that the calm-day ‘€” windy-day signal is a proxy for degree of urbanization. If this turns out not to be the case, how would that affect your broader conclusions?” [Personally, I think this is a non-issue, for reasons explained in #266. However, since Steve McIntyre keeps returning to this point, we may as well get it on the table.]

I think there are rather few scientists who would be willing to do a data dump on their experiments, any more than a normal person would welcome a week-long visit on one-day’s notice, even from a friend. I think it makes much more sense to approach someone in a way that doesn’t sound like preparation for a trial, and which expresses confidence in his professional attitude and capability.

– There is some question as to how the global trend of UHI averages to zero, although the trends of nearly all regions for which trends are reported are positive.

No.

There is some question as to how the global trend of ‘windy-calm’ averages to zero, given the large magnitude and areal extent of the reported positive trends vs that of the reported negative trends.

There is some other question as to whether ‘windy-calm’ trends mean anything at all with respect to UHI … and if so what that meaning is both alleged to be by (Parker 2006) and actually is, for all values (positive, zero, negative) of ‘windy-calm’ trends.

And following from those two, there is a further question as to whether or not averaging local, daily ‘windy-calm’ trends to larger scales (temporal or spatial) is a legitimate exercise at all, given the meanings assigned to such trends.

Those would be a good start. Others, such as …

There is some question as to how it is possible to assign each station day to one of two windspeed classifications that are defined by a tercile binning.

… you might want to ask later, so as to not appear to be preparing for a trial or something. 🙂

I would be interested in disaggregated results by site and by year. What Parker reported were averages, which can be misleading if outliers, multiple populations, or un-modeled processes are present. Specifically, it would help to have a list of stations with identifying metadata and (for each station and year): TminCalm, TminWindy, TmaxCalm, TmaxWindy.

Although I don’t expect to find anything earth-shattering, I think it would be interesting to check the sensitivity of the conclusions to choices of statistical approach. Parker considered simple autocorrelation, but the this sort of data also exhibits homoscedasticity, as well as spatial and long-term-temporal correlations that likely need to be addressed, too. Given the structure of variability in similar datasets, it seems likely that GLS (generalized least squares), at the least, would be appropriate.

However, that conclusion might depend on one’s purpose. For reasons that Neal has touched on (in particular, the beneficial cancellation that occurs in “crossing the river twice”), the best strategy for estimating the component of UHI in estimated global temperature trends during the last 50 years is likely not the same strategy as is needed to provide insight into the physics of UHI. It might turn out that Parker’s approach addresses the first problem while doing nothing for the second.

I think there are rather few scientists who would be willing to do a data dump on their experiments, any more than a normal person would welcome a week-long visit on one-day’s notice, even from a friend. I think it makes much more sense to approach someone in a way that doesn’t sound like preparation for a trial, and which expresses confidence in his professional attitude and capability.

Let us be clear what I/we? am/are referring to here in terms of data requests. My reference was the data needed to repeat the Parker analysis and as far as I know this is a reasonable request of any author of a scientific paper and should in most cases be readily if not immediately available — and particularly so when the data has important, if not critical impacts. Requests such as for data and replies to my other queries are, in the real world, more frequently made by those who are skeptical of the conclusions than those who are in total or near total agreement with them. I would think that the author on considering these requests is well aware of the adversarial nature of the request.

Most scientists that I have known are more than happy and willing to show how they reached their conclusions, so I guess your continual cautioning to use a “kid gloves” approach is a bit puzzling to me. A scientist, such as Parker, deserves a request presented in a respectful and polite manner, but who in turn may not put much stock in a request presented in an overly deferential manner.

There are three reasons why I would not favor the approach you propose:

1) Introducing oneself with a “gimme all your data, and if I want your opinion I’ll beat it out of you” approach establishes a relationship that is not merely adversarial but hostile. That has the tendency to shut the door to further discussion.

2) For a scientist, being approached respectfully should not be taken as being overly deferential, if the right questions are being asked. In my experience, the best way of establishing your credibility with technical people is not by attempting to bully or one-up them, but by focusing with laser-sharp focus on the technical issues and potential weak points of the argument. That puts both of you on the same side: staring critically at the problem, not at each other. If both of you are trying to understand what is going on, you are partners, and people will go through some effort for partners, even short-term partners. But if your approach is, “I know you’re wrong, and I’m trying to find the evidence for that,” the interaction is completely different, and likely to provide far less information.

3) Scientists are generally more than willing to explain how they have come to their conclusions. But that doesn’t mean that they are ready or willing to turn over raw data:
– Depending upon the size of the group (and he doesn’t acknowledge any assistants), he may have taken shortcuts in how the data are formatted and organized. To me, that suggests that he might have to do quite a bit of work to prepare his raw data in such a way that it would be presentable to an outsider, instead of being highly confusing or even misleading. The fact that he didn’t do a 100% analysis of all the data that he had available suggests some kind of resource limitation (probably time) to me, so shortcuts are more likely. He probably did as much as he thought was necessary to answer his questions. In the same way, most people don’t prepare each tax return with the expectation of getting an IRS audit: They do a good-enough job that, if they are challenged, they can put together a reasonable picture, with some extra effort. That does not mean that they would be ready to go on a moment’s notice; and they don’t seem to look forward to these occasions of self-justification, either.
– Finally, even the collection of the raw data, much more the different stages of analysis, represent a considerable degree of intellectual effort. Science is a competitive enterprise, and scientists will prefer to milk the last drop of significance from their data before releasing it to the world: this is their livelihood.

If you take these three points into consideration, you might see two things in the questions I have asked:
– I have been polite, but I have also focused on the specific issues that, assuming that Parker’s work is valid and carefully done, are still puzzling. These are questions that should interest him as well.
– I have also asked these questions in an open-ended way, so that Parker gets a chance to explain what he was thinking about. This aspect is the weakest point of his papers: His parsimony in explaining his motivation for adopting certain strategies. (My impression is that this is a combination of responding to the journal’s space limitations, page charges, and the British style of understatement.) I suspect that many of his methodological peculiarities will be much better understood when we have an understanding of what he was trying to do, and what constraints he was operating within.
– Finally, going in politely at the beginning does not preclude screaming and yelling later, if information is not forthcoming. The reverse is not the case.

1) You correct my question to something I would paraphrase as: “There is some question as to how the global trend of ‘windy – calm’ averages to zero, although the magnitude and extent of the positive-trending areas would seem to exceed those of the negative-trending areas. Can you provide some insight on that?”

2) “Whether ‘windy – calm’ measurements mean anything at all…” From the perspective of my posting #278, I would have to regard that as hostile: This is already discussed in his two papers, and would signal that you find unacceptable his general rationale – not the way to encourage an informative response. Better to get him to explain what he was about, and clarify detailed (and focused) issues in that context.

3) The question about whether it is legitimate to average local daily ‘windy – calm’ trends to larger scales: ditto.

4) Assignment of a station day to one of two terciles: Actually, if you look carefully at Parker’s papers, he talks about ‘windy’, ‘calm’ and ‘all’ days. There is no problem in omitting the data points of the middle tercile, as long as you still have a dense-enough collection of windy and calm days that you can do a good interpolation (see #15).

For reasons discussed in #278, I think that the application of alternative statistical methods & approaches would have to come much later.

Even though the questions are legitimate, I think, again, that it will be much easier to get Parker to talk about what he was trying to do and why, than to gt him to present fully prepared numbers for an alternative analysis.

Neal, you’ve mentioned a couple of times that Parker would be amenable to a set of questions which doesn’t set off alarm bells.

I’m afraid that that’s probably not possible at this point. I’d be astounded if he has not be alerted by one or more likely several people that he’s being debated on ClimateAudit. Sure from a public view ClimateAudit is under the radar, but for the cogniscenti I’m sure it’s watched carefully. And I think Steve or some others here have sent questions or requests already. In fact, I’m sure of it as that’s what one thread not long before this one was precisely about; to compliment him and someone else for responding promptly and favorably to data requests.

Therefore I’m sure he knows what’s going on and likely is keeping a bit of an eye on things as he surely knows of Steve from the M&M vs MM wars.

Neal J. King, I believe, as DD indicated #282, Steve M has already requested the data and Parker has agreed to follow up that request. I’ll agree to disagree with how you in how we classify the appropriateness of a formal request, and, since you have agreed to present questions in a manner that at least according to your standards would not allow an excuse for less than a full reply due to potential “hurt feelings” or “aggressive asking” or whatever non-issue might be offered, I will throw the challenge to you and await the replies from Parker to your queries’€” but not for a long, long time.

Perhaps your and my experiences have been with a different variety of scientists. I always found that the truly committed scientists wanted to talk, write, explain, defend and generally show off their data and the analyses of it to the point of doing so at something less than a drop of hat and without provocation or prodding. Therefore when I hear of scientists who show hesitancies in these activities, pardon me, if I wonder.

I will revise the questions in the next day or so and post them for further review. I’ll send them after that.

My point is that I don’t know Parker, wouldn’t recognize him if I passed him in the street. I have known a range of scientists, some very good; and some of them have somewhat quirky personalities. What I am proposing is pretty careful, in that I think even a quirkier than average scientist should be encouraged to reply positively and fully; and a more extroverted one would not feel I was beating about the bush.

The point is that I don’t want to screw up the probing by making assumptions about the personality of the author.

Gentlemen. If Parker is, or has read this blog he will be laughing his socks off. You are talking as children planning a plot against a parent unaware the parent is standing outside your bedroom door. I’ll ask…. no I will…. no I want to…. but you asked last time…. so?

If I am a subject on a blog I go there in person. eg eli with his knmi analysis where he tries to defend the giss homogenisation.
Parker OTOH is hiding is his ivory tower, hoping that the thunderstorm will drift away.

The point is that I don’t want to screw up the probing by making assumptions about the personality of the author.

Neal, I understand your concerns in erring on the side of caution. I look forward to your questions of Parker and his responses and thank you in advance for your efforts.

Gentlemen. If Parker is, or has read this blog he will be laughing his socks off. You are talking as children planning a plot against a parent unaware the parent is standing outside your bedroom door. I’ll ask…. no I will…. no I want to…. but you asked last time…. so?

No, Paul, it is you who is laughing his socks off. Even at my advanced age I have sufficient “child like” sensitivities to realize what people are really saying. As for Parker, it does not matter what his reactions are to these preparations. We will learn from his responses and from a lack of response on his part.

Neal, I’ve written to Parker and asked him for information about the data used. To date, he’s been very prompt in replying and reasonably helpful. Personally I draw a distinction between asking for for data and methods and asking why he did things a certain way. I think that an author should have archived data or data citations and methods at the time of publication, as in econometrics, and if he failed to do so (as climate science has poor practices) then he has an obligation to take the time to provide the requested information (and should perhaps think about making a proper archive.)

I don’t think that Parker has any obligation to chit-chat about why he did things. If he wants to do so, he’s free to do so. You can obviously ask him whatever you want as an individual, but again my own policy (and what I’ve conveyed at this site) is to limit inquiries to specific things that should be archived in a proper system. Authors should not find this confrontational (e.g. Malmgren’s cordial respnose) but unfortunately some do.

While I wish you luck, I don’t plan to involve myself with your request.

You and Parker are different people, and may very well take a different attitude towards discussion. That’s primarily a question of personality. But I don’t think we’re primarily interested in Parker’s personality, are we?

You’re free to ask Parker what you are interested in hearing about. I wouldn’t expect you to take on questions that don’t interest you.

I will ask Parker the questions that have actually arisen in my mind, based on the discussions we have had here. While they will be my questions, I would be very interested in considering any fine-tuning by anyone who may think that I’ve misunderstood or over-emphasized something in the discussion.

I would be interested to hear any fine-tuning of them. (But I don’t guarantee to accept everything, either!)

I will submit them to Parker at a suitable time, depending on feedback. Pity it’s Friday already.

Here goes:

1) We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Are all the 290 sites at airports? Can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

2) There is some question as to how the global trend of windy – calm’ averages to zero, although the magnitude and extent of the positive-trending areas would seem to exceed those of the negative-trending areas. Can you provide some insight on that?

3) Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

4) What is the minimum UHI trend that could be detected, on a global basis?

5) Your study is based upon the understanding that the difference between the calm and windy day/night measurements reflects the UHI and near-surface temperature inversions. An alternative view has been suggested in our discussion: that the windy day/night measurements reflect the influence of air from a broader region, which has a lesser UHI. This is a picture in which we have an urban hotspot surrounded by a larger suburban region, both of which are undergoing an increase in UHI ‘€” but the suburban UHI is always weaker. (This is one reason why the airport question comes up earlier, in 1).) In this view, the trend in the urban UHI would be hidden by the equal trend in the suburban UHI. Can you comment on the feasibility of this interpretation?

6) You have suggested that the calm-day ‘€” windy-day signal is a proxy for degree of urbanization. If this turns out not to be the case, would that affect your broader conclusions? And if so, how?

7) As you know, Roger A. Pielke Sr. has raised an issue with regards to your study, that it does not take into account sufficiently complications concerning the near-surface temperature inversion. (This is what I was get out of it, anyway.) Would these issues be side-stepped by focusing attention on the Tmax measurements instead of the Tmin measurements?

But he never proves that the wind is what blows off the UHI. He left so many factors out of his analysis that you can’t tell what happened.
By leaving out enough factors, I could do a study that proves that wet sidewalks cause rain.

Parker’s claim is that if UHI doesn’t decrease when the wind blows, then there is no UHI.

If on the other hand, what is happening is that turbulence caused by wind is pulling down warmer from air aloft, then that would mask any “blow off” of UHI.

Among many other things, what Parker needed to have done was paired rural and urban sites that were close enough to be under the same wind conditions, and then compared the affects of wind on both of his stations.

There are still the problems of just declaring a day windy or not. I know it’s simpler, but sometimes you can simplify your test beyond all useability. He needed to graph the affect (cooling/warming) based on wind speed.
He needed to demonstrate that the “wind” he was charting was actually the wind that was affecting both the temperature sensor and the urban area being tested.
He needed to graph wind direction vs. affect measured.

The article cited by jae in #300 actually is relevant to that point. I quote the abstract, with emphasis added:

“The village of Barrow (71°N latitude) is the largest native community in the Arctic, with a population of approximately 4500 people. Situated on the coast of the Arctic Ocean in northernmost Alaska, the area is entirely underlain by permafrost. Although most supplies must be imported, Barrow relies on local natural gas fields to meet all energy requirements for building heat and electrical power generation. This energy eventually dissipates into the atmosphere, and can be detected as a pronounced urban heat island (UHI) in winter. Since 2001, a 150 km2 area in and around Barrow has been monitored using ‘ˆ¼70 data loggers recording air temperature at hourly intervals. The mean daily temperature of the urban and rural areas is calculated using a representative sample of core sites, and the UHI magnitude (MUHI) is calculated as the difference in the group averages. The MUHI is most pronounced in winter months (December’€”March), with temperatures in the urban area averaging 2°C warmer than in the surrounding tundra and occasionally exceeding 6°C. The MUHI is maximized under cold and calm conditions, and decreases with wind speed and warmer temperatures. It is strongly and directly correlated to natural gas utilization on a monthly basis.”

So the 4 years of study of Barrow by Hinkel & Nelson support Parker’s assumption that a wind will blow away UHI.

– Parker’s claim is that if the trend of the “calm – windy” signal is not increasing with time, then the trend of UHI is also not increasing. He never claims there is no UHI: in fact, his “calm – windy” measures it to be a non-zero value.

– For measurements of Tmax, why would the air from above be warmer? For measurements of Tmin, it would be, due to the near-surface temperature inversion. If there is no trend in the NSTI, then it still doesn’t affect his conclusions. If, on the other hand, Pielke (as I understand him) is right, and there should be a trend in NSTI, due (ironically) to C-O2 increase, then we can just put aside the Tmin measurements and focus on Tmax measurements. That in itself wouldn’t change his conclusions.

– On pairing rural and urban sites: If someone were to have conveniently arranged to build weather stations 50 years ago in pairs, and made sure that cities were forbidden from being built near one of each pair but allowed to expand near the other, this might be doable. But in the real world, people do not arrange their urban development for the convenience of climatologists. That’s life.

– Details on windiness: The suggestions you’re making are not unreasonable, but they are not the study that he did. They might very well be legitimate areas in which someone could look harder. But without a study that shows that there is an interesting conclusion to be drawn, no one would want to do so much work. So maybe someone (maybe even Parker) will be inspired to do the job more carefully, and see how much of the original conclusion is sustained.

Parker’s claim is that if the trend of the “calm – windy” signal is not increasing with time, then the trend of UHI is also not increasing. He never claims there is no UHI: in fact, his “calm – windy” measures it to be a non-zero value.

I’m confused. If there were only 1000 people in Barrow, would there be as much UHI effect as there is for 4,500 people? It appears to me that the UHI has to increase with increasing population. Ergo, the UHI effect increases with time for most places.

#302, #307 Wouldn’t “trend of the “calm – windy” signal is not increasing with time, then the trend of UHI is also not increasing” be largely dependent on the location of the energy souces with respect to normal wind direction and the location of the station? Assume more and more energy from UHI sources, if the wind does not blow towards the unit, the signal has less chance of expierencing that energey. Conversely, if an area is growing, props to jets, 100 cars to 1000 cars, etc, and the wind blows towards the unit one expects more energy. Since one would assume about 33% of the stations would indicate UHI, counting partial energy increase from overlap and other heat effects, does this mean a.) that we should expect that global warming cooled at a rate of 33% of the estimated total UHI, or b.) that the method lacks the definition to detect a plus/minus 33% increase or decrease? based on his results.

Re #311 Hi, Neal. You’re well-read in Parker while I’ve just begun to read the paper. I’m hoping you can help me with a basic question. I apologize in advance if this has been answered earlier.

My question is, why did Parker chose predominantly high-latitude rural stations to demonstrate a correlation between NCEP reanalysis winds and the actual station observations? It seems more appropriate to choose a variety, with a heavy weighting towards urban stations in lower latitudes.

Higher-latitude stations tend to be windier than those in lower latitudes and tend to be in regions of greater pressure differential, where the NCEP reanalysis is (in my thinking) more likely to represent true wind conditions. Middle and lower latitude locations may well show less correlation, similar to Phoenix AZ in Parker’s Appendix B.

Parker’s conclusions heavily depend on the premise that NCEP computer-generated (pressure differential derived) winds represent reality, a premise that may not be the case in regions of less pressure differential (= lower winds).

As I mentioned in #307:
“The suggestions you’re making are not unreasonable, but they are not the study that he did. They might very well be legitimate areas in which someone could look harder. But without a study that shows that there is an interesting conclusion to be drawn, no one would want to do so much work. So maybe someone (maybe even Parker) will be inspired to do the job more carefully, and see how much of the original conclusion is sustained.”

You ask why higher-latitude stations are apparently over-represented in examining the NCEP reanalysis issue. Appendix C does not explain his choice specifically; however, in the talk he gave in January 2005, he mentioned that he had trouble getting data from some tropical countries: They wouldn’t release the data!

I have asked the general question 1): “We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Are all the 290 sites at airports? Can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?”

The continental US includes Phoenix AZ and possibly Houston TX, two large urban areas, with the rest being smaller places. (Note- the locations are all based on an ocular examination of Figure 1(b) and may be off.)

It’s an odd pattern for a UHI study. Surely lower-latitude, urbanized places like the UK, Europe, Australia, Japan, Singapore, South Africa, etc could have offered historical wind data as easily as 1950-2000 Siberia.

I think two fair questions would be, “What is the basis for choosing the 26 stations?” and “Can the same degree of correlation be shown for, say, urban stations in Europe, Japan and Australia?”

If the reanalysis/reality correlation is poor at most stations then Parker’s analysis is doomed. Poor correlation would likely lead to the two trends ( T,windy and T,calm) looking about the same, which is what he presented in the paper.

One meteorological reason that I wonder about a poor correlation is that I expect the effect of wind on nighttime temperature to be greater than the 1C Parker shows. Most of the stations are inland and higher latitude, where things can get quite cool at night if there is little wind to mix the air. I was surprised when I saw only a 1C wind effect.

I think your question in #317 is well-stated.

It would be interesting if he could provide plots of representative cities where actual wind data (not reanalysis data) is available, like say Chicago and Yokohama, and can show a no-difference in long-term trend in specific urban islands using real data.

(As an aside, it would also be interesting if someone skilled in graphics could plot Parker’s stations on an equidistant type global map, rather than the equilatitude projection he uses. My eyeballs tell me that he’s heavily weighted towards the high latitudes of the Northern Hemisphere, moreso than it appears at first glance at his Figure 1.)

2a) In your Table 1, although the global windy trend equals the global calm trend, the calm trend exceeds the windy trend in only one region (Tropics) which only has a small number of stations (about 24), while the windy trend exceeds the calm trend in 5 regions with about 241 stations (Arctic, Europe, Asia, North America, Australasia). What were the weights applied to the individual stations that led to this result. Would you provide the windy and calm time series for the individual stations that contributed to this calculation.

2b) In your Table 1 for North America, the trend for all days exceeds either the calm trend or the windy trend, which doesn’t seem possible.

3) You attribute the following to Johnson et al 1991: “The main impact of any urban warming is expected to be on Tmin on calm nights (Johnson et al. 1991).” However, this statement is not made in Johnson et al 1991 (the word “minimum” does not occur in the article.) They actually say something different: ” For example, most mid-latitude studies show that the heat island intensity (the difference between the temperature of the warmest location in the city and the background rural value) of the near surface air layer reaches its maximum a few hours after sunset on calm. cloudless summer nights (Taesler, 1980: Landsberg, 1981; Oke, 1982).” The temperatures “a few hours after sunset” are not the Tmin temperatures. Without further argument or justification, Johnson et al 1991 do not support the asssumption that Tmin changes are a proxy for urban warming impact. Do you have any alternative references supporting this assumption?

4) While the hypothesis that increasing urbanization will result in a higher trend for calm Tmin than for windy Tmin is a plausible hypothesis, are you aware of any studies that have confirmed this hypothesis. The evidence from your study suggests that the hypothesis is not true, in that the windy trend is greater than the calm trend in 5 regions (Arctic, Europe, North America, Asia and Australasia). Instead of this evidence showing that there is no UHI increase in the main temperature indices, an alternative explanation for the empirical evidence is that, for whatever reason, actual urban changes do not result in a material difference in trend for calm Tmin than for windy Tmin.

Neal, thank you for taking the lead in organizing questions for Parker. I think you bring a renewed sense of civility to Climate Audit (my personal view — “auditors don’t need no stinkin’ permission” — clearly needs some refinement). Though SteveM has always exhibited tremendous courtesy toward everyone, the same cannot be said about all of us.

#322. Johnson here is talking about their models rather than actual UHI, which in Figure 8, follows a different pattern. Still, I take your point and my comment needs to be more nuanced. The real issue is whether Tmin is an accurate rendering of UHI. Here’s a graphic from Hungary showing maximum UHI in summer days. Parker’s test is irrelevant to that .

You suggest, “If the reanalysis/reality correlation is poor at most stations then Parker’s analysis is doomed. Poor correlation would likely lead to the two trends ( T,windy and T,calm) looking about the same, which is what he presented in the paper.”

I would also point out that if the correlation were poor at most stations, not only the trends but the differences (calm – windy) would be washed out. My feeling is that a lack of correlation would hit each one (signal & trend) proportionately.

However, I will modify the text of the question to bring up the issue of how the 26 stations were selected. Watch below.

With respect to your questions 2a) and 2b): I’m fine with the critique, and will modify the text of the question to take that into account. However, as I’ve mentioned before, I do not want to get into asking for a data dump, and certainly not on a “first date”.

My view is that a scientist who is not paranoid should be interested in explaining what he is doing and why; and what peculiarities there might be in his analysis, and why. This he should be willing and eager to do informally – over a beer, as it were. But I can think of plenty of legitimate reasons why someone might not want to go “open kimono” on their data files. I don’t cheat on my taxes, but I don’t want the IRS coming around without plenty of advance notice, thank you.

The other point is that, unless there is some definite reason to be suspicious, I don’t have any interest in doing someone’s arithmetic for them. So, at this stage of the game, I don’t have any use for that data.

As I understand you are communicating with Parker yourself, I suppose you can pursue this matter with him directly.

– The question about Johnson: As discussed previously, I don’t see that Parker’s reading of Johnson, whether he got it right or wrong, is a central issue. In my representation of Parker’s argument in #15, I just assumed that the wind will reduce the UHI and the NSTI effects. This seems to be very much in line with the study of Barrow. Whether it is the biggest effect of the evening or not doesn’t seem to be germane.

– As I understand Parker’s paper, his point was to demonstrate that the global GW trend had not been contaminated by a growing-UHI trend. It is actually irrelevant to his point as to whether this has anything to do with actual urban changes as such (ref. #251).

If you can convince me that these points matters, I will include them; otherwise not.

– In light of Dave Smith’s comments (#314, #318), I have added a question, now numbered 2).

– In light of Steve McIntyre’s comments (#319), I have modified the text for one question and added another. Jim Johnson, you might want to make sure that the concern that you raised has not been wiped out by these modifications.

Here goes:

1) We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Are all the 290 sites at airports? Can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

2) In Appendix C of Parker (2005), you studied the correlation between the actual station wind speeds and the NCEP-NCAR reanalysis values for 26 stations. This is important, since one would ideally have used actual wind speeds at the same time as the temperature readings for all stations and measurements. These 26 stations seem to be higher-latitude stations. Would that give rise to any selection effects?

3) In Table 1 of Parker (2005), although for the global average, the windy trend equals the calm trend, this seems to be true in only one region (Tropics) which has only about 24 stations; whereas the windy trend exceeds the calm trend in 5 regions with about 241 stations (Arctic, Europe, Asia, North America, Australasia). Can you provide some insight on this result?

4) In Table 1 of Parker (2005), for the North American region, the trends for “All” days exceeds those for “Windy” and for “Calm”. This seems a bit odd. Can you clarify that?

5) Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

6) What is the minimum global UHI trend that could be detected, using these methods?

7) Your study is based upon the understanding that the difference between the calm and windy day/night measurements reflects the UHI and near-surface temperature inversions. An alternative view has been suggested in our discussion: that the windy day/night measurements reflect the influence of air from a broader region, which has a lesser UHI. This is a picture in which we have an urban hotspot surrounded by a larger suburban region, both of which are undergoing an increase in UHI ‘€” but the suburban UHI is always weaker. (This is one reason why the airport question comes up earlier, in 1).) In this view, the trend in the urban UHI would be hidden by the equal trend in the suburban UHI. Can you comment on the feasibility of this interpretation?

8) You have suggested that the calm-day ‘€” windy-day signal is a proxy for degree of urbanization. If this turns out not to be the case, would that affect your broader conclusions? And if so, how?

9) As you know, Roger A. Pielke Sr. has raised an issue with regards to your study, that it does not take into account sufficiently complications concerning the near-surface temperature inversion. (This is what I was get out of it, anyway.) Would these issues be side-stepped by focusing attention on the Tmax measurements instead of the Tmin measurements?

You wrote:
“2a) In your Table 1, although the global windy trend equals the global calm trend, the calm trend exceeds the windy trend in only one region (Tropics) which only has a small number of stations (about 24), while the windy trend exceeds the calm trend in 5 regions with about 241 stations (Arctic, Europe, Asia, North America, Australasia).”

which I rephrased as:
“3) In Table 1 of Parker (2005), although for the global average, the windy trend equals the calm trend, this seems to be true in only one region (Tropics) which has only about 24 stations; whereas the windy trend exceeds the calm trend in 5 regions with about 241 stations (Arctic, Europe, Asia, North America, Australasia). Can you provide some insight on this result?”

The measuring and recording of atmospheric temperature at locations around the world for the past century+ has been fundamentally an urban project. Temperatures weren’t (and aren’t, except by means of satellites) measured where people weren’t. So the attempt by IPCC, et al, to minimize the UHI effect is to engage in a a statistically tautalogical exercise. The global temperature record (GISS, etc.) IS the record of the UHI effect. Differences between “rural” and “urban” records are a matter of … degree, not type.

I have followed CA with fascination and admiration for several years now, and applaud the continuing audit process. Properly interpreting the screams of tortured statistics is a crucial endeavor.

Re #324 Hi, Neal. If I look at Parker’s Figure 4 and estimate the typical difference between a calm day and a windy day I get about a 1.2C (2F) difference (0.9C at night plus 0.3C in the afternoon). I think that most meteorologists would tell us that Parker’s 1.2C calm-vs-windy difference is considerably smaller than what actually occurs. A true calm-vs-windy difference is probably several times greater than 1.2C. This indicates to me that Parker’s correlation between reanalysis wind and actual wind is suspect.

Another wrinkle is cloud cover. If, on average, windy days have more cloud cover than calm days then the delta-T mentioned above may be, in part or whole, due to that cloud cover difference. My guess is that, in mid and high latitudes, windy days indeed tend to be cloudier than calm days. I don’t know about the tropics.

So, I don’t know what we’ve got with this paper. I wish Parker had used urban sites having actual wind data, even at the price of analyzing fewer than 260 stations.

– The whole purpose of Parker’s technique is to get a look at the UHI effect as it is normally defined: for example, see the papers by Hinkel et al. on their studies of Barrow, Alaska. UHI is the temperature effect that would be expected from industrial activity, residential heating, heat storage in buildings, people, etc. Now, if you are saying that urban weather sites are displaying temperature rise that is confined to cities but is not due to industrial activity / residential heating / heat storage in buildings / people / etc., then what is it due to, and why shouldn’t the same cause apply to rural areas that don’t have weather stations?

– Steve McIntyre’s suggestion is that what Parker is seeing is a matter of air replacement. My thought about that is that, in order for the air replacement to reflect a greater suburban area rather than a reduction of UHI, one has to assume that the suburban area being drawn upon must be so large that the air must have picked up a lot of heat from the suburban area before reaching the weather station. On some assumptions about how quickly the windy air can pick up heat from suburban land, it should be possible to estimate how big these suburban areas would have to be, in order for this suggestion to be plausible. My intuitive sense is that we would be talking about a quite big area, and I’m not sure it’s reasonable to expect that all 290 of Parker’s sites are going to be embedded in that way.

“3) In Table 1 of Parker (2005), although for the global average, the windy trend equals the calm trend, this seems to be true in only one region (Tropics) which has only about 41 stations; whereas the windy trend exceeds the calm trend in 5 regions with about 224 stations (Arctic, Europe, Asia, North America, Australasia). (These counts are based on information you have provided separately to Steve McIntyre.) Can you provide some insight on this result?”

Both the issues of low correlation and unaccounted-for cloudiness would serve to mis-allocate days, so that the calm-windy signal would be reduced by some factor, so if Parker gets 1.2-C, you would expect that the actual UHI is something larger.

OK, let’s suppose that the factor should be 3.

Then the implication is that the actual UHI trend is not 0 – but 3 * 0. But 3 * 0 is still = 0.

So this boils down to the question of how big a UHI trend could be, without being detected. I already have this question:
“6) What is the minimum global UHI trend that could be detected, using these methods?”

Here is a re-wording:
“3) In Table 1 of Parker (2005), the windy trend exceeds the calm trend in 5 regions (Arctic, Europe, Asia, North America, Australasia), and the calm trend equals the windy trend in only the Tropics region. However, the global average shows that the calm trend equals the windy trend overall. According to information on 265 stations that you have provided separately to Steve McIntyre, there would seem to be 224 stations in the first collection, and 41 in the Tropics; I suppose there should be another 25 which were not included. Can you provide some insight into how this works out?”

Calm days have a greater temperature range than windy days, due to differences in vertical mixing. This is true whether an UHI is involved or not. My guess is that, globally, the calm vs windy spread is perhaps 5C (though it varies considerably among locations).

If Parker is finding only a 1.2C spread, then I suspect that his “calm day” bucket and his “windy day” bucket contain many misallocated days.

Parker offers some station data in the Appendix. Suppose that Phoenix (Figure C1(b) in the appendix) is representative of the global correlation between actual wind and reanalysis wind. As an exercise, take a pencil and divide the dots into thirds along the x-axis and then the y-axis, with 1/3 of the dots being in the “calm” terce, 1/3 in the “normal” terce and 1/3 in the “windy” terce. (The pencil-drawn figure should look like a tic-tac-toe figure.)

The only dots that are properly allocated are those in the lower-left box and in the upper-right box. The other dots (those vertically aligned with the two boxes mentioned above) are misallocated.

As an alternate exercise, estimate the median windspeed in the “calm” reanalysis terce and the “windy” reanalysis terce for Phoenix and see how little difference exists.

Is Phoenix representative of global urban stations? I don’t know. I do suspect, though, that the windy, high-latitude stations Parker examined may not be representative of the globe. It’s a critically-important issue in my mind.

(Regardless, if it doesn’t rain today we’re off to photograph another Texas MMTS station (Brenham) for Anthony’s collection, so no more Parker talk. The GISS plot for Brenham is here . I think we’ll find an unadjusted 1940 station move plus nearby tree encroachment, but no barbeque pits or A/C exhausts. We shall see!)

More importantly, although a single urban region may not result in a large impact on global climate, the collective impact of all urban regions on the global climate system is as yet unknown and unstudied. Jin et al. (2004) show that zonal mean UHI has 1-3 degree warming over the Northern Hemisphere latitudes, implying that the collective UHI may be a significant contributing factor in the overall global warming signal

But that article is a description of a modeling technique, it doesn’t provide further discussion on the statement made concerning collective UHI. Presumably, that would be found in Jin et al. (2004), which should be:

Jin, M. and S. Liang, 2004: Improving Land Surface Emissivity Parameter of Land Surface Model in GCM. Conditionally accepted by Journal of Climate.

Re #339 Thanks again, Neal, for helping people like me sort through the Parker paper. I’m beginning to read the main body of the article and perhaps I can use that to illustrate my concern. Correct me where I err.

Suppose that from 1950 to 2000 there was a 0.5C increase in T(min) due to undetected UHI growth. Using Parker’s method and assuming that the reanalysis wind accurately reflects the true wind I would expect to see Parker’s “calm” and “windy” plots diverge by 0.23C over the 50 years.

Why 0.23C? Well, per Oke (see Steve M’s reference), wind affects UHI in proportion to the inverse square root of the windspeed. An eyeball exam of the data in Parker’s appendix indicates that “windy” days are perhaps 3 times more windy than “calm” days, so using Oke’s approximation we’d expect the plots to diverge by 0.23C.

Now, detecting that 0.23C divergence depends on there being a good relationship between reanalysis windspeed and actual windspeed, such that all points are allocated properly and the spread between calm and windy is at its maximum. However, if the relationship between reanalysis windspeed and actual windspeed is poor, then there are many misallocated points and the spread lessens (dampening occurs.

My suspicion is that the dampening is greater than 50%, but let’s assume it’s 50%. That means that the divergence we’re seeking is now 0.115C, which is small and difficult to detect.

I assumed 0.1C/decade UHI growth, which is very large, and my other assumptions were generous towards detection of a divergence. If I had made assumptions more in line with the Phoenix data then the divergence I was seeking would have been considerably smaller than 0.115C indicated above.

So, to me, any dampening from lack of correlation between reanalysis and actual data is critically important, due to the small size of any underlying signal.

Neal, the ultimate puzzle for me in studies purporting to show that there is no urbanization impact in temperature averages that include airports, urban and suburban sites is that: (1) we know through direct measurement that there is at present substantial UHI in many sites (although the UHI is a product of many factors and population is only a proxy. (2) it is a reasonable belief that similar measurements in 1900, or even 1950, by satellite (had such existed at the time) would have detected a substantially smaller UHI in many, if not all, the sites in the large-scale network. For such sites, if one purports to show by some proxy such as windy trend vs calm trend that there has been no increase in UHI affecting temperature in that part, the only plausible conclusion for me is that the proxy is not measuring some salient aspect of the network.

I agree in both cases that there is a question. That’s why I have posed two relevant questions on the list:
“5) Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

6) What is the minimum global UHI trend that could be detected, using these methods?”

I think these questions get to the heart of the issue. I know you would like to pin him down to specific calculations, but my perspective is that Parker knows his calculations far better than I do. Rather than try to “trick” him into revealing a some hidden bias, I would rather just ask him what he thinks is going on.

Analogy with financial auditing: My brother is a CPA who used to do auditing for one of the Big 4 auditing firms. He predicted years ago that there would be a meltdown in companies relying up on external financial auditing for their justification. Going forward, he predicts that the Sarbanes-Oxley rules will not help: by spelling out so explicitly what the auditors MUST do, these rules, in effect, specify what the auditors must do to AVOID RESPONSIBILITY for their misleading explanation. His prescription was that the auditors should make a simple statement that “the auditing report gives an accurate portrayal of the financial condition of the corporation.” Failure to conform to that would be much easier to prove than by the Sarbanes-Oxley rules.

Along the same line, I prefer to ask Parker what he thinks is going on, and why.

Well, slow Sunday afternoon, so I digitized the Parker NCAR/NCEP and station wind data for Phoenix. There’s a few oddities. He says:

For 18 of the stations, correlations were in the range 0.6’€”0.8; the least well correlated station, Phoenix (Arizona), scored 0.2’€”0.5.

Here’s the data:

Now for the oddities.

Oddity 1) What are we looking at here? The text says:

Station wind speeds for 26 locations in North America and Siberia (triangles in Fig. 1b) were compared with corresponding NCEP’€”NCAR reanalysis daily average winds. The station winds were selected to be for a constant nighttime hour as close to dawn as possible to coincide with the most usual time of Tmin.

Presumably, then, these are individual days. For Phoenix, there are a total of 494 days shown … but over the period of record, there are about 18,600 days. How were these particular days chosen?

Oddity 2) What do the triangles and the crosses represent? I assume that they are pre- and post-1979 data, as this is discussed in the text, but there is no notation.

Oddity 3) There is some bad data shown in the graph, although it is only visible when the graph is enlarged. The Phoenix winds are measured to the nearest 1/2 metre per second, but there are two data points that are in between the 1/2 mps intervals. Won’t affect the results … just makes me nervous when I see that kind of thing.

Oddity 4) He says the correlation of the Phoenix data is “0.2 – 0.5” … what does that mean? If the triangles and crosses are pre- and post-1979, it doesn’t mean that, because both of those groups have a correlation of 0.2 …

In addition, it is worth noting that a correlation of 0.2 means that the NCAR reanalysis figures only explain about 4% of the Phoenix wind variance …

Oddity 5) He says:

Nonetheless, scatterplots show that both before and after 1979, an NCEP’€”NCAR reanalysis daily average wind in terce 1 represented an enhanced likelihood of very light wind being observed at the station, even at poorly correlated Phoenix (Fig. C1).

“Enhanced likelihood” is a curious measurement. Of the days with winds in the NCAR first terce (less than or equal to 2.2 mps), only 45% of them were in the first terce of the actual station winds. While this is higher than terce 2 (39%) or terce 3 (35%), the difference is not statistically significant. The 95% confidence intervals are terce 1, 37% – 52%; terce 2, 33% – 45%; terce 3, 30% – 41%. Because all of these overlap, there is no statistically significant difference between the three groups.

OK, here is my proposed final text for the questions. Since some people may not have seen it over the weekend, I’ll will wait until Tuesday morning (GMT) to send them to Parker. Fine-tuning will be welcomed until then:

1) We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Are all the 290 sites at airports? Can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

2) In Appendix C of Parker (2005), you studied the correlation between the actual station wind speeds and the NCEP-NCAR reanalysis values for 26 stations. This is important, since one would ideally have used actual wind speeds at the same time as the temperature readings for all stations and measurements. These 26 stations seem to be higher-latitude stations. Would that give rise to any selection effects?

3) In Table 1 of Parker (2005), the windy trend exceeds the calm trend in 5 regions (Arctic, Europe, Asia, North America, Australasia), and the calm trend equals the windy trend in only the Tropics region. However, the global average shows that the calm trend equals the windy trend overall. According to information on 265 stations that you have provided separately to Steve McIntyre, there would seem to be 224 stations in the first collection, and 41 in the Tropics; I suppose there should be another 25 which were not included. Can you provide some insight into how this works out?

4) In Table 1 of Parker (2005), for the North American region, the trends for “All” days exceeds those for “Windy” and for “Calm”. This seems a bit odd. Can you clarify that?

5) Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

6) What is the minimum global UHI trend that could be detected, using these methods?

7) Your study is based upon the understanding that the difference between the calm and windy day/night measurements reflects the UHI and near-surface temperature inversions. An alternative view has been suggested in our discussion: that the windy day/night measurements reflect the influence of air from a broader region, which has a lesser UHI. This is a picture in which we have an urban hotspot surrounded by a larger suburban region, both of which are undergoing an increase in UHI ‘€” but the suburban UHI is always weaker. (This is one reason why the airport question comes up earlier, in 1).) In this view, the trend in the urban UHI would be hidden by the equal trend in the suburban UHI. Can you comment on the plausibilitiy of this interpretation?

8) You have suggested that the calm-day ‘€” windy-day signal is a proxy for degree of urbanization. If this turns out not to be the case, would that affect your broader conclusions? And if so, how?
9) As you know, Roger A. Pielke Sr. has raised an issue with regards to your study, that it does not take into account sufficiently complications concerning the near-surface temperature inversion. (This is what I was get out of it, anyway.) Would these issues be side-stepped by focusing attention on the Tmax measurements instead of the Tmin measurements?

The discussion on Pielke has been on the many, many, shortcomings of the study. Stratification is just one of them.
The example of a 100mile wide city was just that. An example. You seem to think that all cities are as small as Barrow Alaska.

The issue still remains, the issue is still not covered by the study.

They study is still useless as anything accept cover for those who’s commitment to the AGW agenda is more important than their committment to the truth.

Neal, We can already answer 1 – no, the 290 sites are not all airports. Certainly his stations in UK, Iceland and Ireland are mostly non-airports.
If, as you say, all the US sites are airports but most of the europe ones are not, this would give a straightforward UHI explanation for why the trend in the US is 0.25 but for europe it is only 0.17! His trends merely show the growth in airports over the last 50 years. So I would like to know what is the trend for the airports compared with the non-airport sites?

The reason I asked about the 290 airports was because someone said that he thought they might be nearly all airports: from #39:

“First, all (or nearly all) the U.S. sites in the Parker network are from airports. I haven’t parsed non-US locations yet, but it’s a fair surmise that nearly all the non-US Parker sites are airports as well.”

– I’ve looked at Pielke’s comments, and I put down what I got out of them. If you can go into more and clearer detail, please do so.

– If Parker’s technique only applies to, say, 280 out of 290 cities, that doesn’t sound too bad to me. How many 100-mile-diameter cities do you think there are?

– For every paper, there are issues that are not covered. Science progresses a bit at a time. A truly scientific approach entails finding out what you can, clarifying open issues, and suggesting further work if you have the imagination.

Neal, the question would be – what proportion are at airports (does he have a list)? Are there any site considerations applicable to airports that could have an impact on his findings? If you’re asking him to expand his views, it would be interesting to get a reconciliation of his results to Jin’s.

The only thing I would add is a question about the categorization of windy/calm.

As I have said before I think his distinction is arbitrary and anthropomorphic.

Essentially, Windiness or a turblant mixing wind is not a simple function of wind velocity.
Depending upon surface geometry a wind of 2m/sec will be a mixing wind, or a 2m/sec wind can
flow laminarly over the entire site and miss a site. If the site in question has SHELTER or wind breaks, then
the velcoity at which that shelter can be breached is dependent upon the geometry of the shelter,
direction of the wind, and wind velocity.

Here is the revised wording for question 1:
“1) We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Can you estimate what proportion of these sites are at airports, and do you think there are any site considerations applicable to airports that would affect your results? In general, can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?”

I’ve only just been able to print out 2 of Jin et al.’s papers, and haven’t had any chance to read them. Since there’s been no real chance to discuss these papers, it seems premature to toss them into the mix right now. Particularly since they don’t seem to reference Parker’s study, anyway.

(By the way, if you could get the last one I mentioned in #343, that seems more likely to contain an alternative view to Parker’s.)

RE: #348 – Prove to me that it will work for broad regions encompassing a web with nodes consisting of proper “cities” and strands consisting of diffuse exurban zones – ala Eastern US, ala Western Europe and increasingly ala SW US coast and parts of Asia.

Oke (1976) showed
that even for a small town of 1000 residents, the typical
UHI effect is in the range of 2° to 2.5°C. This has recently
been confirmed by Torok et al. (2001), who
found that the urban’€”rural temperature differences
may scale with the logarithm of a region’s population.
Such changes on thermodynamics can modify mesoscale
circulations over urban areas (e.g., Wong and
Dirks 1978; Yan and Anthes 1988; Avissar and Pielke
1989; Chen and Dudhia 2001).

I think Parker has addressed the question of windiness to the extent that he can, without expanding the scope of his activities by an order of magnitude:

– I agree that the significance of wind speed can depend on local conditions, but it’s a big jump from compiling available measurements to traveling around the world to measure Reynold’s numbers at 290 sites. His research budget would have to go up by a couple of orders of magnitude, just to cover the airfare.

– Given this limitation, he has done what he can do: fiddled with the definition of “calm” to see what effect it would have. Apparently, it’s not major.

Fair enough. One cannoot ask Parker to investiagte every possibility. So I withdraw My question.
nevertheless, an industrious grad student in search of a thesis could do some interesting work
in this area.

By the by, I have enjoyed our interchange. I hope that you maintain your involvement here.
You made me think. That’s a good thing. just ask my wife.

Parker has a better excuse for not citing Jin et al. than the reverse: if you look at the dates of submission for the manuscripts, Parker likely knew nothing of Jin, but Jin should have been informed about Parker (2004).

In any case, I have a chance to look at Jin, Dickinson & Zhang (2005), “The Footprint of Urban Areas on Global Climate as Characterized by MODIS.” I note the following:

– Jin et al. measure UHI using skin-temperature rather than surface-air temperature. As they state:
“Satellite-measured skin temperatures are related to the surface air temperatures but do not necessarily have the same seasonal and diurnal variations, since they are more coupled to surface energy exchange processes and less to the overlying atmospheric column. Consequently, the UHI effects from skin temperature are shown to be pronounced at both daytime and nighttime, rather than at night as previously suggested from surface air temperature measurements.”
So the results they get don’t directly relate to Parker’s index.

– Jin et al. don’t measure trends over a long period of time. So their results aren’t in a position to contradict Parker’s.

– Finally, one point they cite even provides a possible explanation of why Parker’s results don’t show any significant UHI trend: “Oke (1976) showed that even for a small town of 1000 residents, the typical UHI effect is in the range of 2° to 2.5°C. This has recently been confirmed by Torok et al. (2001), who found that the urban’€”rural temperature differences may scale with the logarithm of a region’s population.”

If the UHI only grows as the logarithm of the population, that could go a long way to explaining why Parker doesn’t see any UHI trend.

And, to keep the eye on the ball, that is all that Parker is claiming: He is not saying that there is no UHI effect, or even no UHI trend. He is just saying that the GW signal is not being caused by a UHI trend, because that trend is not big enough.

If there are any meteorologists on this thread, I’d like to hear from them. In my experience, which covers locations on both coasts of North American, several central locations and various trips to Europe and the South Pacific, wind is not an independent variable. It is part of specific weather events. In particular, wind accompanies the advance of cold fronts, as well as thunderstorms, which are also cooling events. The immediate temperature drop that accompanies these can be 10 deC or more. The cooling effect of rainfall, as in thunderstorms, will be markedly different in urban as compared to rural environments. Parker’s whole thesis fails the test of the real world.

As discussed before, that would cause a reduction in the gap between the windy-day and calm-day graphs. However, one would expect the reduction on the measured difference in trend of these two graphs to be proportional to the reduction of the difference itself.

So since the UHI Parker gets is about 2 times too small (compared to other measurements), I would expect his measured trend difference to be 2 times too small. But since he got essentially zero, 2 x zero still = zero.

So, it would be great to have better data. But I think it’s very plausible that his result is robust against that criticism.

OK, I have sent the letter. Here is the final transmitted form of the questions:

///////////////////////////////////////////////
1. We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Can you estimate what proportion of these sites are at airports, and do you think there are any site considerations applicable to airports that would affect your results? In general, can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

2. In Appendix C of Parker (2005), you studied the correlation between the actual station wind speeds and the NCEP-NCAR reanalysis values for 26 stations. This is important, since one would ideally have used actual wind speeds at the same time as the temperature readings for all stations and measurements. These 26 stations seem to be higher-latitude stations. Would that give rise to any selection effects?

3. In Table 1 of Parker (2005), the windy trend exceeds the calm trend in 5 regions (Arctic, Europe, Asia, North America, Australasia), and the calm trend equals the windy trend in only the Tropics region. However, the global average shows that the calm trend equals the windy trend overall. According to information on 265 stations that you have provided separately to Steve McIntyre, there would seem to be 224 stations in the first collection, and 41 in the Tropics; I suppose there should be another 25 which were not included. Can you provide some insight into how this works out?

4. In Table 1 of Parker (2005), for the North American region, the trends for “All” days exceeds those for “Windy” and for “Calm”. This seems a bit odd. Can you clarify that?

5. Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

6. What is the minimum global UHI trend that could be detected, using these methods?

7. Your study is based upon the understanding that the difference between the calm and windy day/night measurements reflects the UHI and near-surface temperature inversions. An alternative view has been suggested in our discussion: that the windy day/night measurements reflect the influence of air from a broader region, which has a lesser UHI. This is a picture in which we have an urban hotspot surrounded by a larger suburban region, both of which are undergoing an increase in UHI ‘€” but the suburban UHI is always weaker. (This is one reason why the airport question comes up earlier, in 1).) In this view, the trend in the urban UHI would be hidden by the equal trend in the suburban UHI. Can you comment on the plausibility of this interpretation?

8. You have suggested that the calm-day ‘€” windy-day signal is a proxy for degree of urbanization. If this turns out not to be the case, would that affect your broader conclusions? And if so, how?

9. As you know, Roger A. Pielke Sr. has raised an issue with regards to your study, that it does not take into account sufficiently complications concerning the near-surface temperature inversion. (This is what I was get out of it, anyway.) Would these issues be side-stepped by focusing attention on the Tmax measurements instead of the Tmin measurements?

I wonder if anyone else has looked at the data provided by Parker on 2 UK sites a couple of posts back?
The 2 sites are:
Lerwick: 60N, on Shetland Island off the north coast of the UK, rural.
Eskdalemuir: 55N, in Southern Scotland, outside a very small town, rural.
In his paper he also uses a 3rd UK site, CET, available on the web.
CET: 52.5N, an average of three sites, two in towns and one at Manchester airport (since 1974, as discussed elsewhere on CA) – urban.

I have plotted the data and found the trends in degrees C per decade over the period 1954 – 2000 (where all three are available):

Never got a response about this study, which clearly shows a greater heating trend in Central Park than in surrounding rural areas (see the first figure). I know it’s only one location, but the trend is clear–there is a definite UHI effect, even in a semi-rural area within the city. The slides also show some interesting albedo effects.

So since the UHI Parker gets is about 2 times too small (compared to other measurements), I would expect his measured trend difference to be 2 times too small. But since he got essentially zero, 2 x zero still = zero.

I think, Neal, you have noted, in your defense of Parker’s findings, the same simplifying results that I, on the other hand, have found to be a convenience that makes me very skeptical ‘€” at least without more detailed understanding and analyses of the Parker data.

The zero temperature trend differences averaged over the globe for the 50 years, that have been purported to be those must influenced by any potential anthropogenic influences, becomes another simplifying result that can be used to claim that the “average” UHI effect over those critical years was zero. If one were searching for a finding that would in one result allow the AGW advocates to claim that little or no adjustment for UHI is required for there temperature trends this would, no doubt, be it. With a zero trend difference one no longer needs to “calibrate” the results to relate it to a quantifiable UHI temperature effect.

Parker’s result has the same appeal to those already convinced of significant and future adverse AGW in that they can consider it as hard or at least harder evidence for their views. It has much the same appeal as the Mannian HS had a few years ago. I think through similar circumstances and reasons these two findings received something less than normal critical reviews and needed or need to be looked at with a critical if not skeptical eye.

I believe a poster who appeared neutral to favorable to Parker in this discussion suggested that Parker had done a good job of data mining in discovering these results. There is a thin line between data mining and data snooping and in my view depends much on one’s a priors going into the search. Certainly working backwards from a desired conclusion would be considered data snooping and renders the statistical significance of the result, for all practical purposes, impossible to determine.

I’ve got many issues with this but little time to write them out in detail. A few key points”
– It is assumed that wind so strongly mixes out the boundary layer as to eliminate UHI or at very least, greatly reduce it. Flaws in this include assuming that UHI is confined to the boundary layer on a calm day, assuming that we only care about heating of gas molecules, and the portrayal of UHI as a sort of “bulk” ground effect. In fact, UHI is a cumulative effect of numerous “hot spots” (flux sources) simultaneously radiating IR, and conducting heat to adjacent solids, gases and liquids. The flux is what it is and no amount of wind reduces the source energy.
– There seems to be ignorance of the fact that if we mix out the boundary layer, and spread out thermal gradient between source and outer space, nonetheless, the overall gradient remains the same, you’ve only changed the function versus position of the gradient’s derivative.
– In the horizontal sense, mixing out boundary layer will only impact surface measurements in small communities. In megalopolis, or a exurban network, you’ll simply move the heat around but the cumulative temperature rise at a given X,Y will be barely affected. Let me draw an analogy using an enclosed piece of electrical gear. A fan will only help if you can blow external air. If all you do is blow around warm internal air, you will not sufficiently lower T(a) to protect the junctions from exceeding T(j-max).

In fact, UHI is a cumulative effect of numerous “hot spots” (flux sources) simultaneously radiating IR, and conducting heat to adjacent solids, gases and liquids. The flux is what it is and no amount of wind reduces the source energy.

I think this is a very important point. A hot brick continues to radiate energy to the surroundings, no matter how much wind there is. The UHI effect is real and it is that simple, IMHO.

RE: #379 – In fact, in non tropical locations in winter, wind may actually cause the energy flux to increase, since space heating of buildings may need to be run at a higher rate in order to maintain the interior temperture setting. This is because the turbulent flow along the building’s exterior walls and windows will exacerbate the outbound heat flow locally, as well as the inevitable drafts which are impossible to completely seal out – these make people inside the buildings want a higher temperature setting to maintain comfort.

Question:We have noticed that, of the 290 stations included in the analysis, the U.S. sites seem to be at airports. Can you estimate what proportion of these sites are at airports, and do you think there are any site considerations applicable to airports that would affect your results? In general, can you explain why you think that the sampling used in this study is representative for the question of estimating the impact of increasing UHI on a global-warming measure?

I did not have metadata for the sites. Many stations were selected from the GCOS Surface Network (GSN) (Peterson, T.C., Daan, H. and Jones, P.D., “Initial selection of a GCOS surface network.” Bulletin of the American Meteorological Society, 78 2145-2152 (1997)). So stations often satisfy the GSN criteria. You may be able to get GSN metadata from NOAA/NCDC or from http://gosic.org/ios/GCOS_main_page.htm. The reduction of heat-island effects during windy weather should be applicable to airports as well as to other sites, because of vertical as well as horizontal mixing. The global warming rate at the stations used in the analysis, using all days’ data, is the same as that reported using all available stations by Jones, P.D. and A. Moberg, “Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001”, Journal of Climate 16: 206-223 (2003). I noted this in my paper. Therefore the set of stations I used is, as a whole, likely to be representative of the larger sets used by Jones and Moberg and other groups. If it had had more (less) urbanization trend overall than Jones and Moberg, it would likely have had more (less) warming. As it had the same amount of warming, it likely had about the same amount of urbanization trend, given that the stations were spread as worldwide as data availability allowed (all networks have sparser coverage in the tropics). As the windy-night trends equalled the calm-night trends, the urbanization trend must have been small.

Question:In Appendix C of Parker (2005), you studied the correlation between the actual station wind speeds and the NCEP-NCAR reanalysis values for 26 stations. This is important, since one would ideally have used actual wind speeds at the same time as the temperature readings for all stations and measurements. These 26 stations seem to be higher-latitude stations. Would that give rise to any selection effects?

Response:
The choice of 26 stations was limited by availability of data. Reanalysis winds are likely to have been equally representative at other locations owing to the availability of pressure data to control the reanalysis; one exception being in mountainous terrain.

In Table 1 of Parker (2005), the windy trend exceeds the calm trend in 5 regions (Arctic, Europe, Asia, North America, Australasia), and the calm trend equals the windy trend in only the Tropics region. However, the global average shows that the calm trend equals the windy trend overall. According to information on 265 stations that you have provided separately to Steve McIntyre, there would seem to be 224 stations in the first collection, and 41 in the Tropics; I suppose there should be another 25 which were not included. Can you provide some insight into how this works out?

Response:

Some stations in the list were rejected from the regional and global analyses, as tabulated in Appendix B of my paper. Trends do not always combine in a simple linear manner when combining samples, because of nonlinearity in the least-squares process.

In Table 1 of Parker (2005), for the North American region, the trends for “All” days exceeds those for “Windy” and for “Calm”. This seems a bit odd. Can you clarify that?

Response:

“Windy” is the windiest third of days, and “calm” the least windy third. “All” includes everything, including therefore the middle third. The differences are well within the error bars cited in the table. Also, trends do not always combine in a simple linear manner when combining samples, because of nonlinearity in the least-squares process.

Given that there can be no doubt that urbanization is going on, it is a surprise that, on a global level, there seems to be no visible growth in the UHI. Even if it is not comparable to the global-warming signal, one would expect to see something. Can you speculate as to why nothing seems to show up, at the global level?

Response:

The selections of stations made for GSN by Peterson, T.C., Daan, H. and Jones, P.D (1997), and for global monitoring and trend estimation by Jones and Moberg (2003) cited above were carefully made to avoid severe urban biases. I never challenged the reality of urban heat islands, and merely assert that the station selection has largely succeeded in avoiding locations with increasing urban effects.

What is the minimum global UHI trend that could be detected, using these methods?

Response:

From the standard errors in Table 1 of my J Climate paper, the calm-night trends and windy-night trends for the globe have 95% confidence limits (⯠2 standard deviations) of 0.05 and 0.06 deg C per decade. So the difference between calm trends and windy trends for the globe can be estimated with 95% confidence within ‘ˆš(0.052+0.062) ~ 0.078 deg C per decade. If we then assume that nearly all of any urban effect will be concentrated in the calm nights, which were defined as the calmest third of nights, then overall urbanisation trends of about 0.03 deg C per decade (a bit more than a third of 0.078 deg C per decade) in minimum temperature should be readily detectable. If more conservatively we assume that not much more than half the urban warming effect is concentrated in the calm nights with the rest in the intermediate-wind-strength nights, then urbanisation trends of about 0.05 deg C per decade in minimum temperature should be readily detectable. As urbanisation is felt in minimum temperatures much more than in maximum temperatures ‘€” which may even be reduced – an urbanisation trend of 0.025 deg C per decade in mean temperature should be detectable using the more conservative assumption. This is about 10 times smaller than the rates of global warming over land since the late 1970s reported in the IPCC 4th Assessment. The more conservative assumption allows for some stations to be affected by large heat islands which persist to some extent even in windy weather (Morris et al. (J. Applied Meteorology, 40, 169-182 (2001))); but GSN stations will almost always be in smaller settlements than Melbourne, with smaller heat islands easily reduced by any wind, or with no heat island at all. None of the US stations used in my J Climate paper is in a city with a population approaching that of Melbourne (3.8 million). [Note that Morris et al’s true heat island in windy weather is about 0.2 deg C weaker than apparent in their results, because the urban station is about 20m lower than the average of the other stations].

Your study is based upon the understanding that the difference between the calm and windy day/night measurements reflects the UHI and near-surface temperature inversions. An alternative view has been suggested in our discussion: that the windy day/night measurements reflect the influence of air from a broader region, which has a lesser UHI. This is a picture in which we have an urban hotspot surrounded by a larger suburban region, both of which are undergoing an increase in UHI ‘€” but the suburban UHI is always weaker. (This is one reason why the airport question comes up earlier, in 1).) In this view, the trend in the urban UHI would be hidden by the equal trend in the suburban UHI. Can you comment on the plausibility of this interpretation?

Response:

When it is windy the mixing is vertical as well as horizontal, so the urban temperature becomes more representative of the whole air-mass, especially because the generally faster moving air aloft can quickly cross whole cities including suburbs. Therefore the windy-night trends are still highly likely to be less affected by growing urban heat islands than the calm-night trends when the surface air is less connected to the air aloft which is also moving more slowly.

9. As you know, Roger A. Pielke Sr. has raised an issue with regards to your study, that it does not take into account sufficiently complications concerning the near-surface temperature inversion. (This is what I was get out of it, anyway.) Would these issues be side-stepped by focusing attention on the Tmax measurements instead of the Tmin measurements?

Response:

No because urban heat islands are less clear by day: sometimes there is even a cool island. See also A. J. Arnfield, International J. Climatology, 23, 1-26 (2003).

– I am not strong on statistical analysis of data. What is “restricted mle”?

– Linearity of least-squares fitting: I think what he is saying is that if you break a data set into subsets, there may not be an obvious relationship among the slopes of the lines that have been forced to fit these subsets. I suspect this is true, because depending on how the data sets are distributed, the slopes of the lines could be very different.

However, I don’t do a lot of data processing. Maybe you could explain in what sense you find least-squares to be linear?

Neal, I second TAC’s thanks to you for your work and to Dr. Parker for taking the time to respond. Much appreciated.

The question of interest to me is #2, which asks why did the correlation analysis use only 26 stations, concentrated in high latitudes (northern Russia, northern Finland, Alaska, see Figure 1b), when several hundred others should be available for geographical balance.

This is a critically important issue because if the actual/reanalysis wind correlation is poor then the study is meaningless.

Dr Parker replied:

The choice of 26 stations was limited by availability of data. Reanalysis winds are likely to have been equally representative at other locations owing to the availability of pressure data to control the reanalysis; one exception being in mountainous terrain.

The way I read that is that, out of several hundred stations, there were but 26 stations with adequate wind data and they happened to be concentrated in northern Russia and Alaska. That is remarkable.

My conjecture is that, had Parker checked lower-latitude stations, where winds and pressure gradients tend to be weaker, he would have found considerably lower correlations. Nighttime winds in temperate and tropical latitudes tend to “decouple” at night, meaning that near-surface winds lose much of their connection with higher-altitude winds. Regardless of whether my conjecture is right or wrong, the burden falls on the study to show the geographical robustness of any correlation. It did not do that.

Again, a good correlation between reanalysis wind and actual wind is vital to Parker’s methodology and it’s hard to take the study seriously unless a global actual/reanalysis correlation is demonstrated.

#397 Neal, I haven’t worked with RMLE, and in any case I’ll have to re-read what Parker did before I can say anything intelligent. In the meantime, my guess is that Parker computed the trends based on likelihood employing contrasts (calm/windy same-site data). I have seen RMLE used for computing variances in similar circumstances involving random-effects models.

Of course you can fit either linear or non-linear models by least squares. My only point was that the non-linearity arises because of the model, not the fitting procedure, and least squares is linear in the sense that differentiation of the sum of squares wrt parameters — typically employing the chain rule — yields simultaneous linear equations whose solution is the least squares fit.

However, I doubt that’s what Parker was referring to; I was hoping you could shed some light on this.

OK, I did some simple algebra & calculus, and I agree that if I have a set of N values X-i(n), which I am trying to fit to the form

X-i = (m-i)n + b-i

then:

m-i = [E(n x-i(n)) – E(n)E(x-i(n)]/[E(n^2)- (E(n))^2]

b-i = [E(x-i(n))E(n^2) – E(n)E(n x-i(n)]/[E(n^2)- (E(n))^2]

where E() means the average over the range n = 1 to N

So you would expect the slope for the sequence
Z(n) = X(n) + Y(n) to be the sum of the slopes for X and Y. Hence, you would expect averaging to work as well.

However, I guess there could be problems if the separate data sets have different numbers of data points (different N). I think he has something fairly specific in mind: He doesn’t say that slopes don’t combine linearly, he says they don’t always combine linearly.

Well, you can’t expect Parker to go beyond his data. My point of view is that, if he gets results that are interesting, but there may be some holes in it, that should provide some encouragement to look harder to patch the holes and see if the results point in the same direction.

Neal and steven (#401, #402, #404), I have not found a good reference to REML on th eweb, and am awaiting arrival of Diggle [1999] — which I hope explains everything — before commenting on Parker’s (inadequately described) REML method. We may need to ask Parker for more details.

However, I would be surprised if pursuing REML leads to anything important. Parker’s Figure 4 makes it pretty clear that the difference between average temperatures on windy days and average temperatures on calm days is nearly constant over time, at least for his dataset. REML would have to be pretty weird in order to obscure a trend in the difference.

There is still the question of whether the Parker dataset is appropriate for detecting UHI. However, the point that Neal and others have made is that it may not matter with respect to the question at hand: If the Parker dataset is representative of the data used to compute the global temperature trends (not sure about this), and if we believe the UHI effect should be reduced or eliminated on windy days (not sure about this, either), then we can address the question of whether UHI effects have contaminated the global temperature trend signal without addressing the question of whether or not the UHI effect exists (a clever design, IMHO).

What concerns me most about Parker’s results? At this point it is Anthony Watt’s discoveries related to the network of temperature monitoring sites. Uncertainty in data complicates, to say the least, interpretation of subsequent results.

If wind data is available near sites…a good possibility at airport facilities or other stations that were setup for local weather…one could take the weather data, use the data for a rough computation of the transition to turbulent wind speed, divide by 3, use this wind speed as the max, and study the effects of laminar flow and temperature. By using Watts ranking system, compare the worst sites with the best sites, then the next worst sites, etc, until one could not compute a significant difference. This would give a threshold detection value, and would also be useful for determining if the land based record is wrong or has been adjusted incorrectly for microsite problems.

Of course, just comparing the ranked sites against each other could provide good information.

I still have a problem with not finding evidence of such a well known phenomena as UHI.

Something that occurs to me, however: in #402, I assumed that all samples have the same number of data points. In my presentation, they do, because I use interpolation to fill in “windy-night” curves for days that are not windy, and the “calm-night” curves for days that are windy. I did this so we would have a well-defined set of trends to talk about.

In practice, I bet that Parker didn’t do that: He probably took trends directly from the available data points. But since different sites would have different windy and calm nights, that very likely would throw off the linearity of slopes. I’ll think about it some more.

But Parker didn’t not find evidence of UHI: What he didn’t find was any evidence that the temperatures that should be independent of UHI had different trends than the temperatures that should be influenced by it.

Re #411 Neal, I still think the weak link in the Parker study is the use of daily average reanalysis windspeed in place of actual late-night windspeed. If the correlation between reanalysis and actual is poor, then his methodology will find exactly what it did – no evidence of a difference in trends.

Parker’s answer to question #2 makes it sound like actual data is not available (at least that is how I read his reply) but I know that the actual data is available for the US sites and suspect that it’s available for many other global sites, especially in large urban areas.

What he didn’t find was any evidence that the temperatures that should be independent of UHI had different trends than the temperatures that should be influenced by it.

Ok, I’ll bite. If he did not find differnt trends, when UHI, by definition, is a measurable difference, then the direct implication is that he did not find UHI when he should. Or he has essentially invalidated those data as being relevant to any discussion of global warming and UHI, or at a minimum, our ability to measure is suspect.

RE: #414 – Or, his assumption that UHI would be “blown away” by upper tercile velocity winds was wrong. In which case, there would be little measureable difference between windy and non windy. Without that (highly suspect) assumption, Parker’s study is complete crap.

Putting what David Smith says in #413 in a slightly different way, Parker’s method is very susceptible to an error based on his method of classifying days by wind speed. As a thought experiment, suppose he used a table of random numbers to classify days into windy and calm ones. What you would expect to find if he has a large number of days is that the trends would not be statistically different in the two groups of days. The two measures of trend slope would converge to the same number as the number of observations increased. To rule out the interpretation that he has misclassified days by windiness, he needs to show his classification is accurate as a measure of wind speed at each station on each night in the sample. I do not think he has done this to anyone’s satisfaction. I think the question of whether the daily minimum was the best measure to use for his purpose is also a very good one. Can one show that you get the same result using temperatures at 3am? Has there been any follow up analysis to check the robustness of his result in this dimension?

RE: #416 – Granted, this is an extreme example, but illustrative. On the average, over the long haul, the prevailing winds distort the UHI “dome” of NYC into an elongated plume which actually results in UHI contaimination in Western Long Island. Interestingly, the dome, on average, also extends out in every direction. This is not something I found at some “denialist” source, it’s in point of fact in a paper, referenced in the Central Park thread, that was issued by an NYC enviro mitigation group. They were using this to support planting trees and rooftop greenery to help cool the UHI, thereby saving electrical usage. What this points out is that – bear with me, this is a bit conception – “RMS” wind, distorts the UHI. That overall distortion is a cumulative effect of a multitude of individual distorting events. The UHI was not “blown away” it was simply misshapen by each wind event. This points out a huge flaw in Parker’s (il)logic.

Another related flaw of Parker is the seeming notion of mixing out. As if UHI was limited to some thin layer near the surface, and not, as is actually the case, constituting a much larger dome, impacting distances of hundreds of feet in the cases of hamlets, thousands of feet in the case of small towns and miles in the case of cities. So, any “mixing” due to wind is only mixing within the UHI dome! DOH!

When I looked at Fresno winds I found something interesting. between 60 and 70% ofthe time the wind blows
hot from the NW and W. Further the wind never gets that strong, blowing between 1-9mph. To get the upper
tercel you had to consider winds from 3-9 mph. So, windy has a lot of week winds in it.
Not enough to cause verticle mixing

The other windspeeds are consistent with this in temp. trends and differentials. Simply,
you can’t generate heat transfer from the boundary layer unless the wind is A. strong enough
or B. cool enough. right?

RE: #421 – I would imagine that most of the areas of the NH that are more populated than the Gobi or some parts of the Actic, essentially have a continuous series of interlinked UHI affected volumes over them. Is sort of a general adder term, at all locations, which varies depending on population density and the degree of surface / land use mods as well as with wind / time / seasonal and day to day variations in anthropogenic flux. Of course Parker detected only 0.06 degrees delta because he was not measuring UHI versus no UHI – he was measuring variations in UHI over time.

RE: #423 – The only case I know of where these mild sorts of winds would have any sort of cooling effect at all would be where they are coming off of a cold ocean – and that impact would only affect the first couple of miles inland. Winds of this sorts certainly are not going to cause turbulance. At the risk of jumping to conclusions, Parker probably had an axe to grind (an AGW hype axe) when he did his study. If so another scientist working toward a goal outside the realm of science. Sadly, one of many.

1. I’d like some kind of site study that showed the extent ( height as you havementioned countless times)
of the UHI Plume.

2. I’d like some kind of study to show how wind distorts or destroys this plume as a function of velocity
( first order effect AT LEAST)
.
3. I’d like to see GROSS wind direction taken into account. Anecdotaly in every place I’ve lived there have
ben cooling winds and heating winds. one need not represent every point ofthe compass.. Two bins.

So why is variation in UHI important? The effect of UHI on the anomalies and the global temperature variation would seem important. For example, I would think the effects of say the entire urban area that Chicago and outlying urban areas have upon climate would be huge and far reaching, much greater than just the area it covers. It would seem, just on the face of it, that all the warming we see that’s actually there is due to:

1. UHI
2. Number of people
3. Amount of land with vegetation that allows the sun’s energy to reach the ground (or in other words, not a jungle or forest, but mowed grass, farmland, etc)
4. Loss of forest and jungle

Yeah, but this wouldn’t be the first time I’ve called into question the fact that nobody seems to be able to provide either a margin of error or any proof of what’s being measured is in fact ‘exactly’ what it’s being reported as. I doubt the thermometers, even if properly sited, are accurate to better than .1 and maybe not even that. Show me the data and the adjustments is what I say.

But to think that a 20 square mile concrete area isn’t hotter than a 20 square mile forest or high grasses or patch of ocean, or even somebody’s front porch on a couple of acres, is delusional, and thinking that the effects of that concrete (or asphault or mixture) 20 square miles in size doesn’t extend a ways out and up is insane.

I went over the Parker paper again and googled online for more comments, pro and con, about the paper’s content. What I find most surprising about the paper on my re-read and those who seem to have little problem accepting or at least finding no weaknesses of the indirect methodology used to make some rather far reaching conclusions is not that papers such as this one can get published, but the authoritative nature these articles seem to take on and particularly so when they are referenced in the IPCC reports. The point I attempt to make here is not concerned with the how much of an UHI effect is in the global temperature measurements, but how easy it would appear to be to move on when a paper agrees with the consensus without really doing a hard hitting analysis of it.

On re-reading I did pick up Parker’s references to some locations with known and independently verified UHI effects. It is not clear, but he notes that some of these known UHI sites were not confirmed by using his calm/windy methods. Central England did not, but Fairbanks, Alaska did, but then Fairbanks did not or at least showed what Parker called a discontinuity when the effects of cloud cover were included. My point being here that Parker was aware of sites with verifiable UHI effects that could have been used to test the validity of his methods and to calibrate them.

The point made recently in this thread concerning what were the distributions of magnitudes of wind velocities used as windy and calm in Parkers methods is one that I would have expected Parker to have followed up in his initial effort by looking at and publishing the effects he saw at various levels of wind velocities and, of course, some examples of wind velocity distributions typical for arrange of sites. (Did not he mention a gamma distribution for them?) What Parker did do curiously enough was look at the calm end of distribution by comparing the results for the 1/3 lowest velocities with those for the 1/10 lowest velocities. He found no difference as one would expect if the real concern, as expressed in this thread, was how often would the 1/3 highest velocity winds be sufficiently high to blow away the UHI effect. Why did he not look at or write about the high end.

While I agree with David Smith that Parker could use more time specific wind velocity data for Tmin if it were available, he was able to show in 25 sites that using the daily average and time specific velocities did not change his trends for calm and windy.

More basically, I puzzle about Parker’s apparent lack of explanation for the tendency of both the trends in Tmin and Tmax to be higher under his definitions of windy conditions when the data is broken down into regions of the globe. He states that windy conditions can blow away the UHI and thus he has accounted for a one way effect for generating higher Tmin for clear evenings, but what specifically accounts for the negative of that proposition that he uses in his trend calculations. He doesn’t say that there is a negative UHI effect so that would make his calculations in effect averaging two different effects, i.e. blowing away the UHI effect and some other effect that tends to make windy (in Parker’s terms) conditions hotter. I am not even certain that this situation would not require the statistics taking it into account.

Could not one just as easily look at Parker’s global results and use the Tmax temperatures as an indication of the driving influence of windy (in his terms) conditions on temperatures in general and then expect the difference for Tmin to reflect some both the general windy force and that of wind blowing the UHI away? Since under windy conditions at Tmax we obtain a 0.03 degree C/ per decade trend higher than under calm conditions (0.13 versus 0.10) and for Tmin we have a 0.00 degree C/decade (.20 versus 0.20) for windy and calm, could not we, after accounting for the windy condition effect on temperature of 0.03 degrees normalize the Tmin trend for windy conditions to 0.17 degrees C/decade and then compare it to the 0.20 degrees C/decade for calm conditions?

Another point. Is Parker using the average windiness for the day, or the peak windiness. I’ve lived in cities where afternoon showers were common, and for those near the shower, it got quite windy, but only for a few minutes. The rest of the day was dead calm.

After continuing to be bit puzzled about Parker’s justification for using the calm/windy versus Tmin relationship trends as a measure of UHI changes over time, I went back and reviewed Steve M’s comments (see below at bottom of post) introducing the “Parker 2006: An Urban Myth?” thread and some later comments in the thread. I have attempted to do some rather superficial analyses of US cities in my quest to determine how evident the basis used by Parker truly is.

For my analysis I used the Regional Map: Weather Underground linked here for the most recently complete year of 2006.

I looked at some average daily wind velocities for some American cities and then compared them to the wind velocities that occur at the daily minimum temperatures (Tmin). Parker bases a lot of the credence for his study on the evidence of no trend differences in Tmin under windy and calm conditions that he obtained for 25 sites using wind condition at Tmin. He also showed evidence that wind velocities at Tmin correlate reasonably well with average velocities.

In my analysis I found that a wind velocity minimum often occurs at the Tmin and these velocities are in ranges that may not be capable of blowing away the UHI effect. Los Angeles, CA is an extreme example of this condition whereby nearly all the Tmins are associated with a wind velocity equal to 0. Other cities that I have looked at thus far appear to have Tmins in the lower velocity ranges and on those occasions in some of these cities when the velocities are out of the lower ranges they have a few annual readings of very high wind velocities at Tmin that often would appear to be indications of weather front moving in. I see approximately the same correlations of velocities at Tmin versus average velocity as Parker reported, but the distributions for velocity at Tmin is different than that for average velocities. I am not at all certain how one would handle the blowing away of the UHI effects for wind velocities at Tmin versus those velocities that might occur a few hours before Tmin. This seems a particularly important point since Tmin normally occurs after the complete daily dosage of darkness darkness whereby the heat losses from any UHI effect cannot be re-established before Tmin occurs.

I have had to extract the wind velocities at Tmin from graphical presentations with continuous temperatures and wind velocities.

On further thinking I judged that one should be able to determine whether a UHI effect can be detected under calm and windy conditions by looking at major US cities for the summer months (Parker claims that summer conditions should enhance the UHI effects and thus the resulting calm/windy effects on Tmin) and plotting the daily changes in average wind velocity versus the change in Tmin and the change Tmax-Tmin. I also did a plot of daily average wind velocity versus Tmax ‘€”Tmin. Note that I am looking for an UHI effect not a change in this effect as Parker was in his study. Parker in fact alluded to there being an UHI effect but that it had not changed globally over the past 50 years. He did seem to leave himself some wiggle room for reduced current UHI effects due to the judicious placement of temperature measuring sites even within an urban environment. What I found was a bit surprising. The amount of trend and correlation varied rather significantly from city to city, but where a significant trend existed, it was always in the opposite direction to that predicted by Parker, i.e. Tmin was always higher under windier conditions and Tmax- Tmin was always smaller under windier conditions. From these results I suppose one could conclude that there is no UHI in the major US cities (as far as temperature measuring sites go).

To further elucidate this development I now need to return to those few sites that Parker concluded had an UHI trend effect and determine how they fare in my analysis.
I recall that Steve M received some data from Parker and was wondering if that data included the wind velocities that Parker used in his study. I can derive this data from the links to it given by Parker, but this requires extracting in the form of U and V vectors and then calculating a velocity. I am also curious as to how the using the average U and V vectors to calculate an average velocity over a day would compare with calculating the intra day velocities from each set of U and V values and average those velocities.

The role of wind speed on turbulence and advection cannot be incorporated into the model in a simple fashion. As a first approximation, note that delta Tu-r in the air decreases as the inverse square root of the speed (e.g., Oke, 1973; Uno et al., 1988).

Steve M comment:

All this says is that wind attenuates the UHI somewhat. If all other factors (the economist’s ceterius paribus) stayed the same, then one would expect the slope of the trend on windy nights to be a titch lower than the slope on calm nights, depending on the amount of attenuation. But any change in the ceteris paribus could easily change things. Oke mentions that driving in the evening increases turbulence. So if the amount of evening driving increased in the evenings in cities over the past 50 years, then this would obviously impact the comparison.

I challenge the very notion that wind can really blow away UHI. I might accept it reducing it in the case of very small developments. But for anything bigger than a small city, I don’t buy the notion that UHI can be “blown away” at all. I think Parker either innocently believes UHI to be a rather small scale development, vertically, or, cunningly has decided to portray it that way. I challenge the notion that UHI is limited to the boundary layer. I believe it can extend much higher. I also challenge the notion of distinct “islands,” against a “natural” background. I believe the effect to be far more diffuse and distributed. Especially in places like Europe and the Eastern half of North America.

RE: #433 – It would need to create vertical mixing of such an extreme magnitude, that the tubulent flow actually exceeded the extent of the “dome” of UHI. Otherwise, all you’d be doing is slightly tweaking the thermal gradient function within the overall “dome.” I will also grant that a substantial (e.g. over 15 or 20 MPH) wind, may result in elongation and shape distortion of the “dome” into more of a tear drop. But, as I’ve repeatedly noted, the flux, at / near ground, is what it is. Heat flow is what it is. Both heat flow from anthropogenic sources as well as reradiation.

In case anyone might be wondering where I get some of my ideas here, I have been in the electronic hardware business for over 20 years. Although not a “thermal management professional” myself, I’ve been around them. I’ve seen FloTherm models as well as actual data using IR cameras, etc. Domes, tear drops, and things more complex than that, are all things I’ve personally witnessed. Parker seems like he either can’t buy a clue, or, has an agenda and there are many suckers lapping it up.

He is not using average windiness. CRUDELY, he looks at wind speed at the grid level.

Then, he BINS the wind speed into two buckets. Top third. Bottom third.

Top third is “windy”. bottom third is Calm

This is nonsense. the first site I looked at, Fresno, had wind speeds that were distributed
in a Poission fashion. High wind speed (8mph) was a rare event. Binning the TOP THIRD of the wind speed
required that one define Windy as something around 3MPH. You dont feel wind chill at that windspeed, much
less blow the UHI away.

I will give my diabolical conclusion. PARKER PROVED THIS: the wind rarely blows hard enough to get rid of UHI.

I went back to Regional Map: Weather Underground linked in my previous post for the most recently complete year of 2006 and found that I could extract the data I needed to determine how the 13 Parker sites (that he claimed his windy/calm trend showed an increasing UHI effect) would perform in my analysis. As in my previous analysis, I plotted the daily changes in average wind velocity versus the change in Tmin and the change Tmax-Tmin and also did a plot of daily average wind velocity versus Tmax ‘€”Tmin.

What I found for the Parker’s 13 sites where he found an UHI effect was basically the same as what I found from my previous studies of major US cities and that was that the correlations and trends in the plot lines were opposite of what one would expect for windier conditions blowing away an UHI effect as assumed by Parker. Only one site went with the trend of Tmax ‘€”Tmin increasing with average wind velocity that would be suggested by Parker’s assumptions and that was for the Alert, NU Canada site. The third relationship at the Alert site went the other way with the change in Tmin increasing under changes to windier conditions.

I certainly do not claim that my superficial analysis here even approaches the level of a peer reviewed and published paper, but is more the result of the frustration of having to except the basic science of Parker’s assumption without any concerted efforts by Parker to answer the basic question to any degree of satisfaction. I find this a general problem in climate science where the basic principles involved in relationships are too often skimmed over by authors in what appears to be a hurried effort to show relationships that have great potential and opportunity for over fitting and data snooping. It reminds of the efforts or lack thereof of the dendros in using pre-selected samples and then regressing with first TRs for the yearly temperature and than a combination of TRs and MXDs for selected months of the year ‘€” all without clearly delineating the basic biological processes involved.

Balling and Brazel (1987b) analyzed long-term temperature records from the cooperative network in Phoenix. Twelve stations were selected, all of which experienced location changes of less than a kilometer and elevation changes of less than 30 m over their period of operation … Averages of daily temperature maxima and minima for the summer months of June, July, and August were examined for trends over the period 1949-1985.

The slope of Summertime Average of Daily Maxima varied from 0.029 to 0.073 degrees K/year. The slope of Summertime Average of Daily Minima varied from -0.036 to 0.114 degrees K/year.

Parker made a grave error. He assumed that winds above X velocity would effectivly “blow away” UHI to such a great degree, that, when “windy” nights were compared with “still” ones, you’d expect to see a “significant” difference in temperatures.

To understand why this is wrong, consider the following situations:
– A field of interfering radio frequency sources, somewhat mapped to population density. Would we expect there to be significant differnces in measured interference levels based on variations in the earth’s magnetic field, and, the ionosphere? In other words, would we expect the intereference to be “blown away?”
– A field of radiactive sources per the above. Would we expect the signal from these to be “blown away” by changes in the absorbtive environment and changes in flux densities of subatomic particles in the background?

As stated well back on this thread, it appears to me that Parker establishes a straw man by somehow averring that lack of significant differences in temperatures between windy and calm summer nights in urban areas shows that UHI effects are minimal.

The whole idea of UHI as a weather phenomenon has to do with the difference in temperature between urban areas and rural areas irrespective of urban winds. 50 years of experience in forecasting and observing temperatures at various locations with both urban and rural targets clearly shows that there is a marked difference in temperatures between the two. The maximum differences occur not in summer, but in winter time (longer nights) under clear skies and calm winds when such a combination of events are observed at the rural stations. Urban differences under similar conditions are much less whether or not there is wind and even more so when skies are overcast. So the critical temperature, the one displaying the largest urban/rural difference is Tmin in winter. (Add a snow-cover and you can have readings over 20° F different between the rural and urban site even when both points are in the same airmass).

The fundamental question is whether these cooler rural readings change appreciably as the site becomes urbanized and to what extent this occurs. Most of the Parker thesis is simply bafflegab.

To determine these differences it is important to survey areas where the weather stations, the urban one in particular, are not located near a geophysical boundary where surface temperatures in one direction are significantly different from those in other directions. This is especially relevant in coastal cities where ocean temperatures can be vastly different from those over the adjacent land areas. [An earlier post, #19, noted that Boston vs rural temperatures differed depending on whether there was an east (onshore) wind or a west (offshore) wind. This is simply a result of Bostons famous sea breeze effect which any Redsox fan can attest to, and not really relevant to the Parker theses]. Useful sites to examine would be in interior areas where large bodies of water or adjacent geophysical discontinuities are not in play.

When satellite mapping coverage of the globe was well under way, I heard it said (sorry about the hearsay) that the East part of one Hawaian island could not be snapped because of near-permanent cloud cover. If this is the case, would it have utility as a place where cloud effects in equations could be minimised?

May I put my 2 bobs worth in and ask if anyone has considered the construction of the modern city in relation to a city built 100 years ago??
The modern home is usually solid brick with a tile roof, or iron. The modern freeway is a concrete & bitumen monster, (anyone laid down on a bitumen road in summer fixing their jalopy?)
100 years ago most homes built here in OZ were timber with a shingle or iron roof. Not much heat retained there. The roads were gravel or dirt. Over the 100 years we have evolved into using more bricks, more concrete roof tiles, more bitumen and more concrete footpaths, freeway dividers etc.
is your area the same??? cheers.

The general method in IPCC-promoted studies of temperature phenomena is:

1. Posit a given measurement as a definitive test of a non-AGW temperature phenomenon.
2. Devise a method poor enough to fail to correlate the measurement with the phenomenon.
3. Fail to find a correlation, proving that the non-AGW phenomenon is not present.
4. Deduce that any trend in the measurement must be due to AGW.

Parker is a good example. Point 1 is that windy nights test whether there is UHI. Point 2 drags in a variety of gross approximations and disregards confounding factors. Instead of looking at really windy nights, take wind speed in terciles so that even the top tercile has many nights with insufficient wind to affect UHI. Insert noise by using reanalysis of daily winds instead of actual wind measurements around the time temperature minima are reached. Ignore wind direction. Make a hash of the classification of stations, etc. With enough of this type of bungling, the correlation virtually disappears, and point 4 is a cakewalk.

Parker is good at this, but he is not the supreme exponent of the technique. The real geniuses are at the National Climate Data Center  Easterling, Petersen and co. Their 1997 paper in Science (Jones and Parker were among the co-authors) will probably go down in history as the worst approach ever to finding the UHI  or the best approach to not finding it. Again, an indirect measure is used  this time it was changes in daily temperature range (DTR) between urban and rural stations. Since UHI definitely reduces DTR, by warming nights more than days, you need a really bad method to fail to correlate the two.

But they managed it. Main steps as follows. First, as in Parker, avoid looking at extreme cases that would give the game away  instead, divide the stations into just two groups, which is even better than terciles for blurring the picture. Second, as UHI increases steeply up to about 30,000 population, set the bar at 50,000, leaving a pile of stations in your rural group that already have substantial UHI. Help this muddle along by using population data 30 years out of date, shifting even more urban warming into the non-urban group. Next, use a one-off measurement of population, not the change over time, as your UHI proxy. This gives you a nice apples and pears comparison  changes in DTR versus absolute UHI at a certain point in time. Lastly, just in case there might be some correlation left, botch the arithmetic so you scatter random errors through the whole calculation. Hey presto: UHI does not reduce DTR. DTR can now be cited on the first page of next IPCC Working Group 1 Report as if it were evidence of AGW! (last bullet point on numbered page 2 here: http://www.ipcc.ch/pub/spm22-01.pdf; further details from Warwick Hughes at http://www.warwickhughes.com/climate/easterling.htm).

Most other AGW-promoting studies use some variant of the general method above. The hockey stick was a bit more complicated, but not much. It set up tree rings as a measure of annual temperature. That was even more dubious than wind and temperature, what with the confounding effects of rainfall, tree disease or damage, insolation, seasonality, non-linear responses etc. So it was easy to find all noise and no trend for 1000 years. The tricky bit was to discover innovative statistical methods to give an uptick in the 20th century. But as they knew what they were looking for, this was not impossible  just invalid. Once they had a thousand years of noise and a spurious recent uptick, it was easy to jump to step 4, and be NOT able to think of any other explanation for the exceptional recent temperature rise than our old friend AGW.

Even the fingerprint studies of the cause of global temperature change since 1850 follow a rather similar pattern: leave out half the natural variables, make unproven assumptions about aerosols etc. and you can soon fail to find any other explanation for warming that our old pal of molecular weight 44.

In all cases, the key steps are to invalidate some non-AGW factor using an incompetent technique, and then finger AGW as the sole reasonable explanation of the data. It boils down to botch and switch.

BTW, its not that Parker and his mates are being dishonest. They are just people who know the right answer before they start their investigation, and who lack the rigour and technical expertise to see that their methods are scientifically invalid. Remember too that they are respected figures in bureaucracies in which received wisdom passes for truth, and where the incentive structure places less weight on scientific discovery and accuracy than on getting things published, defending the party line, and swinging next years budget increase. If any of us were in Parkers shoes, wed behave the same way  or wed be out.

November 4th, 2007 at 1:42 pm
The general method in IPCC-promoted studies of temperature phenomena is:

1. Posit a given measurement as a definitive test of a non-AGW temperature phenomenon.
2. Devise a method poor enough to fail to correlate the measurement with the phenomenon.
3. Fail to find a correlation, proving that the non-AGW phenomenon is not present.
4. Deduce that any trend in the measurement must be due to AGW.

I have never seen it explained any better. Thanks for the road map. It will definitely help me.

1. Posit a given measurement as a definitive test of a non-AGW temperature phenomenon.
2. Devise a method poor enough to fail to correlate the measurement with the phenomenon.
3. Fail to find a correlation, proving that the non-AGW phenomenon is not present.
4. Deduce that any trend in the measurement must be due to AGW.

I don’t think it’s that passive, at least in the case of the HS and the MWP. Mann invented the cherry-picking automaton.

Google “Corrugated Iron” and you will see that this is a common usage for corrugated galvanised mild steel. I don’t think the term iron includes steel in modern usage so “corrugated iron” is probably an historical hang over.

Usually I think of corrugated iron or steel (or aluminum), as being the sine wavey sort of medal. But the old roofs of houses had sections a couple or three feet wide with a crimp like ^ in the center and rounded or v crimps on the sides so that the pieces could be interlocked and (assuming they were installed correctly), would allow rain and melting snow to run off.

I don’t think “greed for fame” is the explanation. This has to do with faith based belief. The belief system is using AGW to support its core dogma, which is man is fouling the planet (man is evil). Hence any thing that appears to support that belief will be accepted and anything that appears to disprove it will be ignored or disparaged. Since this is tanamount to a secular religion, those with fanatical belief in the dogma will take anyone who questions that dogma or anything that appears to disprove it as heretical. Why would someone act this way. Eric Hoffer used the term “True Beliver” . Joining a cause greater than oneself gives you a sense of both belonging and worth. Why do you think an empty suit like Gore is drawn to this? Without something like this he has no purpose in life.

Another factor is what I call “rewards and punishments” . I contend that if you look at an organiztion and try to determine why it functions the way it does, you don’t look at its mission statement or organization chart. Look to see how each individual’s actions are rewarded or punished. If following the rules are rewarded and initiative punished then that will be how the members of the group will act. Look at any goverment organization that provides a service (the local DMV is a prime example), if non performance is not punished and performance is not rewarded you get a non responsive organization. Why do I bring this up? Science has become a bueraucracy. Getting a paper out is more important than producing a good result. Work that supports the right dogma gets promotions and grants. My quess would be that papers with positive results far out number those with negatives, because negative results don’t get rewarded even though they can be very useful. Would Mann have published a paper that said that the methodology he used is ineffective for determining temperature because the MWP did not appear in his results? Would he have tried to determine if alternate explanations would explain the data, instead of attacking those who question his results? No because it did not fit either his world view or his enviornment.

#447
Larry.
we call it corrugated iron, some refer to it as a TIN roof. Very thin, 2-4 mm (??) thick. It used to be painted a reflective silver or galvanised, now it is coated in various colours to suit the area and taste.
I’m just thinking along the line that all are concerned with the expansion of urban areas and foilage etc. But what about the physical attributes of those expanding constructions?? Inner commercial areas are all conctrete and glass. Residential areas are what have changed the most in 100 years in terms of materials & appearance as well as size. Thers another bogeyman to consider. The small 3 room house of the 1940’s to the McMansions of today. (subprimed, of course)
regards.

I’ve said it before and I’ll say it again; when you take some type of land and change it into some other type of land, it acts differently, will affect the weather in some way and thus has an effect upon the climate. What way is that? Depends on what it was before and what it is now. How can we know what it is? I say, it’s the same as the famous “second paper” — We don’t know the answer, and there might not even be one.

I feel as though the IPCC is correct when they say “land use changes and burning fossil fuels” And we should focus on the first. If one assumes for the sake of argument that a global temperature mean anomaly is meaningful and applicable, what would cause the warming (or state it as “what is the cause of the presently observed warming”)?

1. Land use change.
2. Pollution from buring fossil fuels – ground and air.
3. GHG and how they react with each other and the first two.

This current fascination with one of the GHG as the cause is strange to me. The first two probably are most of whatever it is we’re seeing. (And probably in reality, whatever it is we’re actually seeing (warming, cooling, nothing) is rather immaterial.)

If there is a UHI effect in the spatial domain (and no one disputes this), then it had to grow over time. Any failure to detect the time trend must indicate a faulty method, as space and time are inseparable. Seems you’ve pin-pointed the fault. (Note how Parker seems to have fooled himself, thrice, into believing his own rhetoric! Once when writing the proposal, once when writing/revising the paper, and once when replying to a CA auditor.) Now the question is: what is the true magnitude of the trend?

Truth be told, a problem of this calibre would have kept me in any Phd program. I don’t
know why more kids don’t flock to take down the pillars of AGW. Bender didn’t you want
to do something audacious as a grad student ?( ……stupid question )

I find the strange mix of people here refreshing. Glad to see you back, and Dr. Browning, Dr. Lucia is a wonderful
addition.

Side note. A long while back Dr Curry asked you about a 9 year hamming filter for hurricane junk.
I think you missed it. But lucia gave a lucid reponse. too late for Judith to see I think. At some point
I’d like to hear you all talk about Lucia’s POV on this matter. The filtering/smoothing stuff makes
the hair on my arms stand up.

mosher,
You are aok. I saw the problem in Parker’s reasoning but was far too timid to say anything. Afraid to stir the pot too much. You not only got guts, you audited down the problem to a very specific thing. That’s tenacity. Cheers to you.

RE: #477 – UHI is sort of like a rising tide coming up a nearly flat shore profile. It eventually covers almost everything except the most truely desolate places. Think of each human’s “stuff and work” as a series of heaters. As goes human population so go the number of heaters. There has been a lag in heater growth in the third world, but they are catching up, maybe moreso given air conditioning. How many heaters (and where) in 1800, 1850, 1900, 1950 and 2000? Also, there are some interesting heaters. A ski lift motor, an irrigaation pump, and automated freeway sign on I-80 in the middle o Placer county, a high tension power line.

Steve:
The Aleo article is a pretty good summary of some of the issues with the existing GISS data. Is it worth its own thread or is it redundant with other threads and covers ground familiar with most readers?

As an avid motorcyclist for the last 30 years I can vouch that the ‘urban’ effect is real – so is the ‘river’ effect (riding by a river is demonstrably cooler than one km away from that river), the ‘black tar’ vs ‘white concrete’ road effect, and the ‘sunny’ vs ‘cloudy’ day effect…

On another note, is tracking the ‘high’ and ‘low’ temperature on a daily basis even relevant at all? For example, would recording the highest temperature and the lowest temperature over the course of a year and then averaging give you anything at all indicative of the actual climate for that year?

when combined, these factors are sometimes referred to as “urban morphology” … “urban morphology (the hottest zones in the city are those with the tallest and the highest density of buildings, without green spaces and with intense generation of heat from traffic, commerce and services);”

local weather: winds can affect as can cloud cover; large urban centers create their own upwelling and local microclimate …

“anti-rural factors” – lack of evaporative cooling from soil and plants, lack of plant cooling, increased thermal conductivity and capacity from artificial structures, faster water runoff and sequestration in cities

The primary problem between IPCC and actual science is population-based:

“Peterson and others support the IPCC viewpoint at towns with less than 10,000 populations are towns without the need for adjustment for urbanization. Oke (1973 and Torok et al (2001) show that even towns with populations of 1000 people have urban heating of about 2.2 C compared to the nearby rural countryside. Since the UHI increases as the logarithm of the population …”

“Doug Hoyt has noted, in 1900, world population is 1 billion and in 2000, it is 6 billion for an increase of a factor of six. If the surface measuring stations are randomly distributed and respond to this population increase … we note that UHIs occur only on land or 29% of the Earth’s surface … the net global warming would be 0.29*1.7 or 0.49 C which is close the observed warming. It is not out of the realm of possibility that most of the twentieth century warming was urban heat islands.”

[…] temperature record and its many possible biases, and well before David Parker’s non empirical UHI studies sought to minimize the effect based on windy -vs- non windy days (which now appear to be falsified by the new NOAA experimental work), J. Murray Mitchell published […]